{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e39c598",
   "metadata": {},
   "source": [
    "Refs：[Financial Tick Data Analysis with pandas](https://gist.github.com/yhilpisch/f905bfc3e4859a65d736b6593f268ef3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3aacac36",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pylab import plt\n",
    "import swifter\n",
    "from pylab import plt\n",
    "plt.style.use('seaborn')\n",
    "pd.set_option('mode.chained_assignment', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "54814eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Code-Quant\\\\MyCodes'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "53a15697",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./nif_refinitiv_tick_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3ba879af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#RIC</th>\n",
       "      <th>Domain</th>\n",
       "      <th>Date-Time</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Bid Price</th>\n",
       "      <th>Bid Size</th>\n",
       "      <th>Ask Price</th>\n",
       "      <th>Ask Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NIFc1</td>\n",
       "      <td>Market Price</td>\n",
       "      <td>2019-08-01T09:15:01.197913842+0530</td>\n",
       "      <td>Trade</td>\n",
       "      <td>11065.95</td>\n",
       "      <td>9975.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NIFc1</td>\n",
       "      <td>Market Price</td>\n",
       "      <td>2019-08-01T09:15:01.197913842+0530</td>\n",
       "      <td>Quote</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11063.45</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11065.9</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NIFc1</td>\n",
       "      <td>Market Price</td>\n",
       "      <td>2019-08-01T09:15:02.266641886+0530</td>\n",
       "      <td>Trade</td>\n",
       "      <td>11069.40</td>\n",
       "      <td>11775.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NIFc1</td>\n",
       "      <td>Market Price</td>\n",
       "      <td>2019-08-01T09:15:02.266641886+0530</td>\n",
       "      <td>Quote</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11065.90</td>\n",
       "      <td>525.0</td>\n",
       "      <td>11066.4</td>\n",
       "      <td>525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NIFc1</td>\n",
       "      <td>Market Price</td>\n",
       "      <td>2019-08-01T09:15:03.301959220+0530</td>\n",
       "      <td>Trade</td>\n",
       "      <td>11066.55</td>\n",
       "      <td>11550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #RIC        Domain                           Date-Time   Type     Price  \\\n",
       "0  NIFc1  Market Price  2019-08-01T09:15:01.197913842+0530  Trade  11065.95   \n",
       "1  NIFc1  Market Price  2019-08-01T09:15:01.197913842+0530  Quote       NaN   \n",
       "2  NIFc1  Market Price  2019-08-01T09:15:02.266641886+0530  Trade  11069.40   \n",
       "3  NIFc1  Market Price  2019-08-01T09:15:02.266641886+0530  Quote       NaN   \n",
       "4  NIFc1  Market Price  2019-08-01T09:15:03.301959220+0530  Trade  11066.55   \n",
       "\n",
       "    Volume  Bid Price  Bid Size  Ask Price  Ask Size  \n",
       "0   9975.0        NaN       NaN        NaN       NaN  \n",
       "1      NaN   11063.45     150.0    11065.9     150.0  \n",
       "2  11775.0        NaN       NaN        NaN       NaN  \n",
       "3      NaN   11065.90     525.0    11066.4     525.0  \n",
       "4  11550.0        NaN       NaN        NaN       NaN  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1552d96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = data[['Date-Time', 'Price', 'Volume']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3797911",
   "metadata": {},
   "source": [
    "### 对时间轴的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d9ddef0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data['Date-Time'] = pd.to_datetime(new_data['Date-Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "178c48b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date-Time</th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-08-01 09:15:01.197913842+05:30</td>\n",
       "      <td>11065.95</td>\n",
       "      <td>9975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-08-01 09:15:01.197913842+05:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-08-01 09:15:02.266641886+05:30</td>\n",
       "      <td>11069.40</td>\n",
       "      <td>11775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-08-01 09:15:02.266641886+05:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-08-01 09:15:03.301959220+05:30</td>\n",
       "      <td>11066.55</td>\n",
       "      <td>11550.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Date-Time     Price   Volume\n",
       "0 2019-08-01 09:15:01.197913842+05:30  11065.95   9975.0\n",
       "1 2019-08-01 09:15:01.197913842+05:30       NaN      NaN\n",
       "2 2019-08-01 09:15:02.266641886+05:30  11069.40  11775.0\n",
       "3 2019-08-01 09:15:02.266641886+05:30       NaN      NaN\n",
       "4 2019-08-01 09:15:03.301959220+05:30  11066.55  11550.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889d92b5",
   "metadata": {},
   "source": [
    "设置 时间 为 `index` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1e846498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:15:01.197913842+05:30</th>\n",
       "      <td>11065.95</td>\n",
       "      <td>9975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:15:01.197913842+05:30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:15:02.266641886+05:30</th>\n",
       "      <td>11069.40</td>\n",
       "      <td>11775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:15:02.266641886+05:30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:15:03.301959220+05:30</th>\n",
       "      <td>11066.55</td>\n",
       "      <td>11550.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Price   Volume\n",
       "Date-Time                                             \n",
       "2019-08-01 09:15:01.197913842+05:30  11065.95   9975.0\n",
       "2019-08-01 09:15:01.197913842+05:30       NaN      NaN\n",
       "2019-08-01 09:15:02.266641886+05:30  11069.40  11775.0\n",
       "2019-08-01 09:15:02.266641886+05:30       NaN      NaN\n",
       "2019-08-01 09:15:03.301959220+05:30  11066.55  11550.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.set_index('Date-Time', inplace=True)\n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fb5100f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 10108751 entries, 2019-08-01 09:15:01.197913842+05:30 to 2020-08-31 15:30:05.141130971+05:30\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Dtype  \n",
      "---  ------  -----  \n",
      " 0   Price   float64\n",
      " 1   Volume  float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 231.4 MB\n"
     ]
    }
   ],
   "source": [
    "new_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8f595223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date-Time'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data['Price'].iloc[:100000].plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa799978",
   "metadata": {},
   "source": [
    "### Daily Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e7d220",
   "metadata": {},
   "source": [
    "- 增加采样频率为 1天，\n",
    "- label='right': 在降采样时，如何设置聚合值的标签，例如，9：30-9：35会被标记成9：30还是9：35,默认9：35\n",
    "[link](https://blog.csdn.net/weixin_40426830/article/details/111512471)\n",
    "- last(): 方法根据指定的值选择最后 n 行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2db09161",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 800 ms\n"
     ]
    }
   ],
   "source": [
    "%time daily = new_data.resample('D', label='right').last().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "094e60b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 267 entries, 2019-08-02 00:00:00+05:30 to 2020-09-01 00:00:00+05:30\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Price   267 non-null    float64\n",
      " 1   Volume  267 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 6.3 KB\n"
     ]
    }
   ],
   "source": [
    "daily.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2458c6cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(267, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-02 00:00:00+05:30</th>\n",
       "      <td>11027.00</td>\n",
       "      <td>3300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-03 00:00:00+05:30</th>\n",
       "      <td>11019.60</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-06 00:00:00+05:30</th>\n",
       "      <td>10896.00</td>\n",
       "      <td>675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-07 00:00:00+05:30</th>\n",
       "      <td>10963.55</td>\n",
       "      <td>1425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 00:00:00+05:30</th>\n",
       "      <td>10863.10</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price  Volume\n",
       "Date-Time                                  \n",
       "2019-08-02 00:00:00+05:30  11027.00  3300.0\n",
       "2019-08-03 00:00:00+05:30  11019.60   600.0\n",
       "2019-08-06 00:00:00+05:30  10896.00   675.0\n",
       "2019-08-07 00:00:00+05:30  10963.55  1425.0\n",
       "2019-08-08 00:00:00+05:30  10863.10   300.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(daily.shape)\n",
    "daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "47aadeb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date-Time'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily['Price'].plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1fc07ea",
   "metadata": {},
   "source": [
    "### Intraday Bars"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e87b436a",
   "metadata": {},
   "source": [
    "- resample().sum() 重采样时间内求和 [link](https://www.jianshu.com/p/8d3d612afbb2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "168d9278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "resam = pd.DataFrame()\n",
    "resam['Price'] = new_data['Price'].resample('1min', label='right').last().dropna()\n",
    "resam['Volume'] = new_data['Volume'].resample('1min', label='right').sum().dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "65ddd46c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:16:00+05:30</th>\n",
       "      <td>11078.30</td>\n",
       "      <td>216150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:17:00+05:30</th>\n",
       "      <td>11064.55</td>\n",
       "      <td>139350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:18:00+05:30</th>\n",
       "      <td>11070.40</td>\n",
       "      <td>127950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:19:00+05:30</th>\n",
       "      <td>11078.05</td>\n",
       "      <td>84000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:20:00+05:30</th>\n",
       "      <td>11082.50</td>\n",
       "      <td>104700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price    Volume\n",
       "Date-Time                                    \n",
       "2019-08-01 09:16:00+05:30  11078.30  216150.0\n",
       "2019-08-01 09:17:00+05:30  11064.55  139350.0\n",
       "2019-08-01 09:18:00+05:30  11070.40  127950.0\n",
       "2019-08-01 09:19:00+05:30  11078.05   84000.0\n",
       "2019-08-01 09:20:00+05:30  11082.50  104700.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resam.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5123ddfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 99944 entries, 2019-08-01 09:16:00+05:30 to 2020-08-31 15:31:00+05:30\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Price   99944 non-null  float64\n",
      " 1   Volume  99944 non-null  float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 2.3 MB\n"
     ]
    }
   ],
   "source": [
    "resam.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "31d214f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date-Time'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAFjCAYAAAAD725WAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABez0lEQVR4nO3dd2CTdf4H8HdG994tdNAWCi1Q9h4Cigw3KnB4IOc69UTx8Hfw8wQVF+cJB4LCT0U9RUVQD1SUE5QhuxQoUHZp6QC6926S3x9pnuTJaJO0zWjfr39Mnjx58uVr0nzyHZ+PRKVSqUBERERENiG1dwOIiIiIuhIGX0REREQ2xOCLiIiIyIYYfBERERHZEIMvIiIiIhti8EVERERkQ3J7N8BchYWV9m6C3QUEeKK0tMbezXA67DfrsN8sw/6yHvvOOuw369mi70JCfEw+xpEvJyKXy+zdBKfEfrMO+80y7C/rse+sw36znr37jsEXERERkQ0x+CIiIiKyIQZfRERERDbE4IuIiIjIhhh8EREREdkQgy8iIiIiG2LwRURERGRDDL6IiIiIbIjBFxEREZENMfgiIiIisiGnqe1IRM7ldEYxbpbUYHS/cJy4VAiVSoWEKH9EBHlBoVQiI68C8d19IZPyNyARdS0MvoioQ6zemgYA2PzrZdHxeVN747OdFwEAg3oFY8H9yTZvGxGRPTH4IqJ2pVSpcD6r1OTjmsALAE5eLrJFk4iIHArH+4lIUNfQJLqvUqnMet6u4zm4lFMGAPh2bwZWfn3K5Lmzb+1lbfOIiDoFBl9EXViTQomislrU1jfh2s1KPL1qP9Y0TxfWNTTh0X/swXf7r7Z4jfoGBb7afRkrvjiBJoUSPx/NNnmuTCrB7cOiIJVI2vXfQUTkTDjtSNSFPfHPvQbH0jKKUd+owKULBQCAHw9lYcb4OJPXqKnXjpat3HzK5Hl+3q6YN6U3AMDdVSZ6HhFRV8Lgi6iTyi+pwfXiagzqFQIAUCpVkEqNjzglxwfh6vUKVNU2AgCeWrkP86YnCo+XVdXD39vN6HN1L3kxpwxDeodALpMiKSYA2w5korSyHm88PgIRQV7CeQy8iKgrY/BF1All51filU9SAAA9u/shPNATB87cAABEhnhh+aMjROcvfHAAcguqsOzjY8KxWp0AadXXaVj+6HCjr/XbiTzh9p2jY3DvuDhhWnHcgG5QKJVmp5N48/NUxEb44g+3cV0YEXVeXPNF1MkUlNUKgRcAXMkrFwIvAMgtrEZNXROaFErR89xdZaL7W3VSROQWVhl9rUs5ZfjhUJZwf8b4eIP1XMYCrzH9ww2OKVUqXMkrx67jOVAolQaPtyeVSoW8omoozdxQQETUnhh8EXUiF7NLsWTDYdGxd58bh5kTe4qOlVfXQ6EQBx7B/h54ad5Qk9c+m1lscCy/pMaqdk4fGSPcPpdVglOXi3DykjbtxA8Hs6y6bmsKympxOP0m9pzMw9KPjmJnC5sDiIg6CoMvok5kw/fpovuL5wyCt4cLpgyPEh1fv+0sfjuRC0A9DakR183X5LXXbzuLvKJq0bE9J7VTjv9aMNbsduqu/3pn8ym8++1pFFfUCcd+OJSFK7nlZl/PXEs2HMaHP5zDtt8zAQAnLhW2+2sQEbWGwRdRJ1Je1SC63zs6AAAgkUjQs7ufcDy3sBpb92YIt3U9NDlBuP3kPX2F27X1CqzZmoaKGu1rNDWPnnm4yeHn5WpRW+8a3UN0X5MJ381VBqjUGfK/2n1Z2ATQnjTXDA/0bPdrExG1hgvuiTqJX1NzRfdH9xOvq3px7hAA6vVOj729B5rlTjK9HZC3DonEpMHd4RfghYqyGvSLDYJSpcLu4zn4/mAW3vvuDF6YPRAucpkwWjWwZ5DF7e0bGyhaL6ahUKgwfVQMdhy+hl3Hc7DreA4+XjLJ4uvrq6kzDOIighh8EZHtceSLqJP4Ytcl4fbghBA8ekei0fMkEokob5ex2ooSiQRuLuoF+J7ucnh7uOCesbEYnhiKy7nl+PM7+7D229PCjsjqOstTR0SFehs93qRQ4p6xsaJj3x/MxCMrfhPtwDRGqVIht6DKYCF9YVktnln9u8H5WTcqLWw1EVHbMfgispHv9mfgkRW/Ye23p9t8rUs5Zfhs5wXkFhoGGgDw1L19IWkhi/yovtpRMd01Xy2RSCQY0z9CuH/ychHim9eIaZKnWsLDTY6lDxsu8Pf1coVcJv7TpFmjdf6a6ZqRAPDRD+ew7ONj2Lrniuh4dr7x3ZqpzWu+8oqqsfnXy2hSKFFd14ite69g4doDqKhuMPo8IqK24LQjUQcqr6pHTX0TAn3c8eOhawDExaTzCqsQ4OMOT3fLPoorvjgBANh76jqkEokoAPto8cRWy/e46aSVCPAxnjzVGP0pyroGBTzcZBZdQ1dshOEC/7/9YRAA4M7RPfCj3rRkfaOixesdOZcPAPjvsRzMmqTNFSaXme6PlAsFWL/tLACge7AXPvn5gvDYzqPZmDmpp6mnEhFZhSNfRB3o+XUH8fcPj+KpVfsMHquoacDSjcfwzOr9wrHa+iazi1kDwJCEEFHgFeDjZlbdRN0gqqURMn36I1KF5bUI9vOw6BotmTmxJ7oFq0fiJg7qbvC4i0yK7w+qs+a35oLOKJmPp3YzwGt6yWI1gRcANCnFfb/zGFNREFH7Y/BF1EG+2n3Z5GN7T+UhTWcETKlSYfXWNPzlX/vx6D/2mP0af5nRHwsfHCDcNycoAQB3VzmeuCsJr/xpmNmvBRiOfDU0KhHs527RNfTpToEm9QgQbhsbDXx/21ls+z0T6747Y/CY/hTh21+dFG7nl6rzkbm7ytA9xBt/mtZHeEx3d+cWvenKWRz1IqIOwGlHog6y63iOycc+23lRdP8xvYDrfFYJEnsEmvU6uikkLDGyr2GW+daUG1kD5SJv22+4R+9IxP23xMHVRQZvDxfhuJuLDO88PRovvH/I4DmZNyoMjr3x+XGDYzuPZqNbsBeONxcJf2G2ekpzbHKEUIdyUK8QYbNCfYN4WvNKbjmmGK+qRERkNY58UZdW36jAvlN5+OdXJ9HYZHlJG5VKhWPn842uRWopYWm/2EDcNjTS5OP/3HzK5GP6KRN0R4hWP2t+olNr+Hoa5vI6dr6gTdeUSiUI9HUXBV4agb7mj6p5exi2bcueK1i9NU1YZxcdpt5hKZFI8NidSULRcVNSmYSViDoAR76oS1KqVFi/7SxSL2q/XC9kl6J/nGX5qtZ8cxqnM9Rld/RzUZXoZGwP8nXH8keH4y//Uq/vevTOJLi7yrD7uDg3V2tUKpXRlAn/84dBUKpURoOj9iQzsnBds0bL3roHeyHzRgXe+vNIvP7v45g4OBLRod746cg1ZN1Up5TQX7OmERvhg0wTaSeu5JajZ6R1o4tERMaYNfKVlpaGuXPnAgDOnz+POXPmYO7cuXj00UdRVKT+RbllyxbMmDEDM2fOxJ496imUkpISPPLII5gzZw4WLlyI2tpak+cS2dLpK8WiwAuAWQvVDa6Toa13qJ/yoUwn2/zofuHwcJPj3nGx6BPtDz8vV7i5yDDXwhQNdTrTYq89NkK4nRgTgL5mTlO2hbGkpNf1Sg7Zy5nm2pNyqRRrF47HjPFxGNonVAi8WvJ3vZqW942LFZLSfvXrJRbgJqJ21Wrw9eGHH+Kll15Cfb16Ie8bb7yBpUuX4vPPP8fkyZPx4YcforCwEJ9//jk2b96MjRs3YtWqVWhoaMD777+PO++8E19++SWSkpLw9ddfmzyXyBZ+T7uOXSk5uF5sGDBI27hhT3/dli7NFOTdY2LxtzmDhePGdvTpe/8/Z4S8Vbr1D7vbYcTJ1UVmcCwmzKdDX/ONx0cY3RjwTXN5JA1NaSWpFf8jpRIJpo2MFu7fNSYWPbv7YURSGDJvVOJI+k2Lr0lEZEqrwVd0dDTWrl0r3F+1ahUSE9WZsxUKBdzc3HD69GkMGjQIrq6u8PHxQXR0NC5cuIDU1FSMGzcOADB+/HgcOnTI5LlEtvDJzxfw1a+XDWogAsClFgo5r/z6FB5Z8ZvoWJPCvDViL88fhuR4y8vvKJUqKFUqHL9YiJ+PqlMefH8wy+LrtCepRIKNiyeKplj1s9G3t4ggL0SH+eDxO5NEx386cs3o+S3l9GpJQ6Ph/88HbomHi1yKb/ZmoK7B8iz+RETGtLrma8qUKcjN1a5LCQ0NBQCcOHECmzZtwhdffIHff/8dPj7aX79eXl6oqqpCVVWVcNzLywuVlZWiY7rntiYgwBNyueGv7q4mJKRjRxk6q5AQH9GCemM7EbcfyMRj9yWjurYRRWW1iNFJAJqeWQIAKKtrQq8odTqEuxZtN7jGtaIaDE0Mg0qlgkQCJPYIxND+3Vps28aXJuPR13cZHA8K9kadTjmd66V10E1DZYv3gjmvMWF4TJt3PJrj7ok+GJQUjqff1gbBmvZ99tM54VhcjDjQnTIyBv89cg1jBnRr8d/TqNB2rua8kBAfzJjYE1/vuoR9Z27ij1ONl2zSfx5Zjn1nHfab9ezZd1YtuP/pp5+wfv16fPDBBwgMDIS3tzeqq7XTONXV1fDx8RGOu7u7o7q6Gr6+vibPbU1pc56eriwkxAeFhaxFZy6VSgUVgOBgH/y477JQosaU5PggFBZWYuG7v6OiphEvzRuK3ak5mDVRm+vpl0NZKEyowjsmdiO++tERfLxkEhRKJVQqQKVUtfr/TAJgxZ9HYsUXJ/Di3CF495vTyC2sxmc/pqOyRjtCt+yDw8Lao6hQ7w5/L7T2fnt5/jDkl9agrNR2a77c9WI8Tfu2/qrNqVZQUCFK+vrg+DiMSgxttc/cdAJI3fNu6R+O/x7Owte7LmHr7st484kRCA0wXPvGz6f12HfWYb9ZzxZ911JwZ/HP1e3bt2PTpk34/PPPERUVBQBITk5Gamoq6uvrUVlZiYyMDCQkJGDw4MHYt0+d2Xv//v0YMmSIyXOJ2ts/vjyJx/6xB/f+z/f46MfzKKsyTEA6fWSMcDunQD0CW1GjTuXw+mfHcSQ9H+9+q03oufNYNt7adEI0ipbUIwALZvQXXTe/RL25xNTuOn2hAZ5Y9cxYBPt5ILdQHcz8Z/9V0W5I3UXfL80zrIloazHhPhieGGbz151zWy/R/dYWw0ulEkSH+bSahd/DzfjIururHA9MiBde619b0ixoLRGRIYtGvhQKBd544w1ERERgwYIFAIBhw4bh2Wefxdy5czFnzhyoVCo8//zzcHNzw1NPPYXFixdjy5YtCAgIwMqVK+Hp6Wn0XKL2dimnTHT/zSdG4q1NJ0RZ4LNuViA2wheZNypQWlmPjTvOQZ9uQk9vDxeM7R8hKjsz9/beCAvUjoQ89+7vqGwO4Hy9DHNXtcbX0wUVNY144u4kFJTWGh2xs8U0n6PqES7On1ZVo8179vL8YVaXOrptaJTJNXUj+4bjox/PAwDyS2utuj4RkYZZwVdkZCS2bNkCADh27JjRc2bOnImZM2eKjgUHB2Pjxo1mnUvUnvTrI7q5yhDs54GVfxkjWjh/LqsUGxdPxMXsMnz92xUcPNPyrrZ3n1NvINENvrz0koNW1jQiOT4IgxNCMKxPqMVtf+OJkSivakC3YC/U1DVh+4FMMNOBVlSot+i+bqwVE279Gg5vDxfMm9LbaN4y3TQkmkStRETW6ro/n6lT003JAACP3aFdKP3XmdpaiBFBnpBIJOgTE4C/zxuCu8f0EB578Y9DRNdYvcAwe/yIpDB46dUg/OvMAVj44ACMH9ANHm6WL6v0cncRAgBPdzk++ttEg5qKXZmba8dtvJkwqDsSovyNPzZQvXEiO78KJ5n5nojagMEXdToVNQ342/rDomODE7RlZPrFBeHd58ZhZN8wPKOzVksuk+LecXHCff2SQb5e2uzx/3xqNN55ejT+fHdfYZrr2QeSMTIpDP0szJLfGolEgpfna/NcDbAibUVnpmzeAmpNOg9LJMcHC7fXGinsTURkLpYXok4nRa/W4A8r7zHY1eLt4YIn7upr9PmLZg9EbV0TKmtNJ/8N8jOsOTiwZzAG9gw2cnbbRYZ64/9euAW/ncjDqH6WF8TuzBTNwZenFaOMlrAmeSsRkTEMvqhTUalU+GLXJeG+Zo2WJTRlelQqFb745RKq6xwjuaaLXIYpw6NbP7GL0ex27OjgqKKalTiIqH1w2pE6lf8eEydP9fawfLehhkQiwT+eHA1AnU6CHJNm2tGa2pyWqKgRB1/6mzqIiMzF4Is6lS3NNRDbi6e7HBsXT8QLswe163WpfahUKmHasaNHviYO6o6ESD/h/o1iJn4mcjZnrhbjn1+dFP5u2AuDL+pUQv09hNsvzB7YLte0Nm8UdTwVtNOBpy537A5EDzc5/vaQtij6Sx8d7dDXI6L2pWpOknz+WimOnLlh17Yw+KJOZXTzYvQXZg9EUvPaLeq8VCoVruSpC6LLzKwm0BZSiQR/vtv4Rg0icmwpF7SbsYor7JssmcEXdSr1Ter0EK4uLMLeFaReLMS3+64CABbc37+Vs9uHsSSsROTYqmob8aXOZqwmnRJx9sDgi5xGXUMTjp3PR5PC9IdGoVDP48tlnCrsCj7beVG4HR1qfXZ7S4QHGhbVJiLHtuW3K0LdXgCob2TwRWSWjTvOY8P2dOxKyTF5jq12vpF99Y1VTynX1GvTgNgqD5duXc3qusYWziQie6tvVGDl16dw4MwNROuUJmtsUrTwrI7H4IuMqqlrQnpmid2202fkleNv6w/hSl65kMcp9aJ6QfXWvRl48/NUnM4oNniekPOJwVen5ij/ey9ll9m7CUTUgs92XkB6ZgkAYP70PsLxBjuPfDHJKhm15ps0XM4tx+xJPTF5WJTNd/x98EM6isrr8ObnqZDLJPD3dhM9fiWvHKu3puHjJZNExzW7h5mNvHNzlOD6bFYJBumUriIix3I4PV+43SPcV7jdwJEvckSXc9U7yDb/dgWP/mMPACCnoApvf3kCjTZYqFhYpi2MHR3mYzInS2llPQB1MPbd/gzsPZkHAC2uCyPnV1vvGFUH9pzIc5i2EJH5GjnyRc6gsUmJlz8+BgBY+tFRrHhyVIe9lm7gNDwxFE/e0w8A8Nd1B1BWJc4yvui9g/BylxuUAHLv4Dp/ZF+aHwf2EuLvLvxA+GZfBube3tuu7SEiyzQ0cuSLnMCf39kr3C4o0+ZHqalrxKGzN6BQtt+viNf+fVy4fc/YWOH20D6hRs/XDbxmTuyJ1x8bIUq2Sp3PQ5MT0DPSD75ergCAUX3DbPr6rz82Urh97Fx+C2cSkb1k3qgw+VhhmX3zfHF4gNrkmdW/AwAyb1TiockJ7XLNnIIq4bZubcbBvUKw+3iu0ec8fW8/ZBdUYspw269PI9u7dUgkbh0SiSt55fj3zgu4Z1ycTV9fvOOR045Ejkj3h/wj0xNFj53PKrF1c0Q48kVWyS+twY3iauH+r6nGg6K2kkm1b9E+MQG4dXCkwTkTB3XH0D6hmDE+noFXF9Ozux9ee5QjnUQkpp9KYkz/cDu1xDiOfJFRvaP8cTGnzOTjyzYe67CF9wlR/rjU/Nq6IwwAMOOWOKigwm8n8oRjYQH84iUiIq23vzop3HZ3lTncD3OOfJFRbq7Gy/MMTgjB43cmwcPE4+1BE3gteWiwQfDl4SbHH2/vjWdmaEvJSJhWguwojBnviRxORp52vdd9OssSNN8psyfbd5MMgy8ySpPA9MW5Q0THH5qcgFH9wvH64yONPa1dJUT5m3xsMHMrkZ0tf2Q4AKBbEIMvIkegVKpw7Hw+/rUlTTi24s8jMXlYlHD//vHqQCyuu5/N26eL045koL5BO1euP1Lr6a5+y+guhLc3R0m4SV1LRLA66GKeLyLH8GtqLr769bLoWLDeelDNTIm9qrdocOSLDFzILhVue7mLgyw3l46bbqyua8QjK34z+/zFcwahf1yQwy2kpK5BJpXCzUWG2nr75gsiIjX9wAsw/HFe1Jyf73pRtcG5tsTgiwycvardguuqs+YqooXplWEmcnCZK7+0Bgua01aYq3d0AJ6fOQDurhzAJfvwcJNx5IvIxtKzSvDLsWyrnrvreA4A4N87zrVnkyzG4IsMnMnUFqzW3SHy8vxhJp/T1mnIjkpVQdSRPNzkqGHwRWQzSqUKKzefwubfrhj88HGmWRAGXySSX1qDglJ15t8+0f7w83bFPWNjMXVENFz1phznTdXuFtnTXFMxO78SO49mWzSffj6rxCB56gf/M8HKfwGR7Xi6yVFb3yR6vyvtvJaEqDM7c1U7OFBV2yjcVqpUOHjmpujcj/420WbtshTna0hQUdMgTDnOm9obEwZ2ByAu8aNrwsDukEkl+OSnCwCA1VvThF2SIf4eGNLbvB2J/9x8yuCYXMbfBeT4PNzkUChVQs670xnFWL01DU/f289kOSwist6ab04Lt09nFOOLXZdw/y1xSM80zFgvdeA0RAy+CABwNrMYq77Wbs/tFxto1vN0FzNqAi8A+O1ErtnBl773nh9v1fOIbM2juYC7Zupx9Vb1Z+j9bWfx8ZJJANTTJI78JUDkiJQqFQ6cvoHk+CD4e7sZPeeLXZcAAN/uuwoASI4Pwl1jeqBbkJfJzWFymRRNio5JEG4JDi8QAODU5SLhdkSQJ4L92pY1ftyACINjBWW1KCqrxXf7r+LJlXuNVpX/y339hC80Ikenea+aWnS/Zc8VPPb2HlTUNNiyWUROp6pWvdt9TfMPmOMXCvDpzxfwjy9PtvJMtYen9sZzDyQjvpsfPNzkJn/wuLk4RtjDbzkCACiU2nUq/eOC2nw9Ywvwl2w4LLqfnlmCQXrJUh0pfxhRaxTNv6DLq4wHVzuPqndknbxUiFuap/GJOiPNukdry/is+UYddKVlFOPxt/cI30n5JTUGr+HmIkO93o93cz9fri4yVNfZf5OMY4SAZHe6wVe/OPOmHFuiNGNUd+13Z7D229OiY72jA9r82kS2cvCseoHv1r0ZBo/pTm1oNqQQdVavf5aKZR8fs+q5N4qrReWAdL+PdGmO6wdeGxebv7B+4YMDAADPzRpoYSvbF0e+CIB6XYpG7xbK+ujz9XIFoB6x0t15ol9R3pSTOtOdsRG+Zr8ukSPJzq80OLZYZ6R3dF/n2QJPZKnv9l9F5g118KRSqSwe/fr7h0fNOu9w+k2jxy15vahQb3y8ZBJCQnxQWGj4ubUVjnwRAOBKXrlw20Vufhb7frGBmDulN5bNH4qwAO06sR8OZpn1/L/P09aOvGtMD7Nfl8iRKJQq5BaI/5CXVtYLtwN93W3dJCKb+fFQlnA7r50zx49L1q4frq61/3Rhe2HwRQAg5PaylEQiwcRB3RHs54G3/jxKOJ5dUNXqcxOi/BHfzQ93j+mBmHAfDIhv+1ozInvZ20Ki4J1WZuMmcjbLNh7D5/+9aPJxlUqF8qp6k4/r0+SXrG9UYMueK21un6Ng8EUd6mJ2Kb7dlwGFkUVgl3LKAAD3jovDy/OHWb1Qk8gR7DlhOviq5G5H6kJaWuP489FsPL/uII5fKAAApF4sbPFamkX2uTo/6G8fFiXk0Zt7e0Jbm2sXXPNFHUqzTdjYDspAX+O5W4icRfdgL2GapUBnV5a+wuZivkRd3TfNm1Pe33YWvaP8cbH5RzgA0f3//eNgvLXpBDQFI05d0a4Pvn1YFLzcXTAqKQzJPZ1zxoTBFwFQJ0vtyLIoxn4JdQvy6rDXI7IFD3fz/oT6ejKFCjm3uoYmrN56Gn+a1gdhgZ5WXeN0RpHo/sWcMnQP8cKT9/RDXX0TokK98eTKfQC0aYdUKhWKymqx4/A14XmaNZT6qYqcCYMvAgDMurUnvtp9GY/ekdhu19T9pXL0XL7B46P7cQcYObewAA9cyVVvVgn0dUNFdQOaFIY/Yu4aY7xEF5GzeHrVfgDA/35wRKjeAMCiOr6rt542OLZ03lBR3eC3nhgJV508XicvF+HY+QJrm+2wuObLCqWV9UbXMDkzWXM2YBd5294Si3Ryp7yrU4PLtTmrcGSIdrSL9RvJ2d02JEq4PX5QJGQm3tOWfEEROZKK6gYUlYs3ZB07r/4x3dikwPFW1my1xlWvDFBYoCcCfNyEeqnl1Q1odIByQO2NI18WKiqrxd82HIaXuxxrF7IGob7EmAAsmNEfZVX1qGtUYOse9fy+i0yKhkYlVACevT8Zu1NzMMBJ5+qJNOQ6P1YmDY3CzsNZRs/TTwpJ5CwWrj1gcGzD9nQcv1DQ5sCrJTV12ryRf587BK98kgIAGG+kdJ0zYvBlIU1Ga0vKE9Q1NOGnI9mYPDQSPp6uHdW0NmmvH+ZSqUQ0D68JvjT9lVdYjYG9gjGwV3D7vCCRHenWJ43t5oe6BuNBlqnjRM5KP/DST7RdW98kqtNr6eiv7ndldJgPQv09UFBWix6dJBk3530sdDm3zOCYSqXCt/syRIlKdT29aj9+PJSF5941/AWhLz2zBMfO56OozLq8W23FdA9E5qvW+XUOAK4mpu3rGXxRJ7P62bGi+yP7honub9xxXnT/6vUK6PvLff1NXl8z7aixaPZA3DsuFmP7d46RLwZfen44mIlTl4tMPh7fzU+4/XvadVTXNSLzRiV2HL6GNz9PbdNr55fUYOXXp7Bhezr+pleEui2UShW27r2CazdNl1IQiqK226uqbVw8EUseGoykHqzZSJ2PXCr+EzqsOfeQvjpOO5ITutlC+hRfvVmcWwZ0E90/cUk8Mqa/6WrR7IEY0tv0bkX9cYAQfw/cPSa206wV5rRjM6VShZKKOvzn90wAEO3m0BWqU0Lnk58v4LP/XhS9GRRKJWRSKW4UVyPQx92iBezf7jMszttWZ68W49j5Ahw4cwM/H8nGy/OHISbcp91fxxSJRIKEKH88ekcSFr13EHeMirHZaxN1tIQof0wY2A0jktS/+iVS4z9fOPJFzsiS5MDdQ7wNjmm+DwEgPatEOD5zYk/07RHY4vWiQr1x1+geSO6klU8YfDVb/u8UZOe3XBKnoLRGNJR6/y1xSLlQIHre42/vxZtPjBQKhUaFit+Q125Wmgx+zmSWiO6rpzOvYlS/cHQPtjwn1iMrfjM4tvLrU/jj7Qk4e7UEf5reR5hm7Oi9WAE+bvjwbxOEDyJRZyCVSjBvah/t/ebYy9/bFWVV2i8uLrgnZ1RR3dj6SToG9QrGSZ2Zo+0HMjFjfDwA4EaxdhQt2K/1WqcSiQT3jY+z6PWdCb8Jm7UWeAHAobPaiup/nzsEd4zqgVf+NBxD9YZOf9Op8ZajV+Pw1U9TTLx+pcGv4//7Ph0/HbmGpR8dNXuxYpNCiSPnbhpNmOrr5Yqq2kZs2J6OA2du4Kcj2qR1muirI5d8MfCizk74MaPz8ZOAC+7JOV0vEn9/6U/53TokEgAwbUQ0AOCxO5NEj+84dA3ns8SDCgDQK9LP4FhXY/a3YVpaGubOnSvc37VrFxYtWiTcP3XqFB588EHMnj0b69atAwAolUosW7YMs2bNwty5c3Ht2jWT5zqyzb9expY9V9AjXL3L4q7RPRDfXfvm6adXOkd34f3Sh4dibLJ4gWB5teFQrmYbrS7dxHK6gV9LnvjnXnzw/Tk89o89Bo9NHNQdMyf2FO67yGUG57T/qi+irkMbfKnw97lDsPDBAXB1lXHakZySpnSWxgMT4uEql+Lhqb0BAA9OiMeCGf2FESoPNzkWzR4onC+VSvDBj+dEG8iWPjwUft4sLWdW8PXhhx/ipZdeQn29uhL566+/jpUrV0Kpk2j05ZdfxsqVK/HVV18hLS0N586dw+7du9HQ0ICvv/4aixYtwooVK0ye68h+ScnBzqPZUDUPD+lunwWAUX3Fmdp1FxHGRvgiMUa82Px5I3lT5LKWg55Nuy7hht4HwVKnrhRhavMvFAD4z/6rwm2mgCRqu+gw9TKDPjEBiO/uh+T4ILi7yLjgnpxS6sVCSCTA3WN6AACS44Ow4YUJuGVgdwDqBKmDEkJEI2LhAdrSQ/eOi0V5VYNoxie2k6SKaCuzgq/o6GisXbtWuD948GC88sorwv2qqio0NDQgOjoaEokEY8eOxaFDh5Camopx48YBAAYOHIizZ8+aPNfRGFujoYk1pXqLavUX1adlFAPQbjvXLMZtibGSJKL2NCiw8otUNFmQ6fftJ0eJ7uvvdqxvVGi3ymt2O3Lgi8hq45O74dkHkjF/mnYdmJurDPUN5ucFJHIEBaU1UChVUKmAe8fF4YP/mYBwM2o6Bvq6YcrwKDw/cwCmjVRvsLIkL2ZXYVbwNWXKFMjl2tGe6dOni/JBVVVVwdtbu7Dcy8sLlZWVBsdlMpnJcx3NnhOGhaA/+CEdgHZRrSmaWm+3DY1qPl8izI2bYio/kMbIvmG4mF2KHw5mGTx2+OxNlFbWi46F+nsg2N/D4Fx972w+JbrP2IvIelKpBAN7BsPdVfv30s1FhrKqBoOcYESO7Ny1UtF9c1M8SCQSzJrUC/3jgiCVSDBhYLfWn9QFtctuR29vb1RXa6fEqqur4evri7q6OtFxpVJp8tzWBAR4Qm50jVL7GNE3HEfTteuqfj6ajftvS4Cnu4twTJP0zdfXAyEh4h2LPp6uBttyA/215z12XzJ+1VmIHxTkLRpBa9BJKNcnJgAX9N74C/8wBM+u2osfDmXBzd0FM29LgIebHFt/vYTPflLvwPxh5T2IDPVGbkEV5k5PREiID7oFe+G6znRlSIgPxg3sjt9PqYPLazcrERLiA6/mOXhfP8N/W2fQGf9NtsB+s4yx/tJsunl/WzreXjDO1k1yGnyvWaej+u2znRfb5TX2nrou3O4XH+RQ/5/t2ZZ2C75cXFyQnZ2NqKgoHDhwAM888wxu3ryJPXv2YPr06Th16hQSEhJMntua0lLTyd7ag4+HuCsqaxrw1c7zmD7SMC9VTXU9CgvFo3USnVVTcpkETQoVDp2+jkk6Uf+6hePwzOrfAQA5eaWiwE7XC7MHGiyYr6mqw6I5g7F43QF889tlXMgsRrdgL/ySkiOcs2zDQfh4qK8Z7O2KwsJKvPKnYVjyf4dRUlGPObf1QmFhJebd3ksIvgCgsLASlc0jZxUVtQb/NmcXEuLT6f5NtsB+s0xr/XU+q4T9aQLfa9axVb+112u4yaUO8//ZFn3XUnDXbnm+Xn31VbzwwgtQKBQYO3YsBgwYgP79++PgwYOYPXs2VCoV3nzzTZPn2ltdvXiNl7eHC/57LBuuRkbb9Nd8AUBFjXZKoX9cEE5eLhLVfQMgCrYKy+oQE248+JKaWHiVFKvdVXk2swRn9fKC6eZX0SR7lMukeOOxkbiYU4p+zc9vKeWDhBOPRERd0mv/Po7MGxXw926/GsTx3XyR0VxaKLUDC3E7G7ODr8jISGzZskW4P2LECIwYMUK4P3DgQNHjACCVSrF8+XKDaxk71958PMWB0PSRMdiy5wq27LlicG7alSKMH2B8HvvPd/dFQ6MCJy8XtbjQfuexbPz57r4Wt7NfXCDOXjXMmwIAj9+VhA9/UO8cVVeEV6/5cnOVITleXMh6YM9gnLpipIwSYy8ioi4p84a2/qJEot6H5etpfJDAXBlGajoSM9wLEnsE4Oej2cL9iYO7Gw28APEIk77hierabgnR/ghtYcH70XP5eGR6H7jIZUaT0C15aDBWfHHC4HhUqLfR4OvROxIxqm84fL1cceJiIaLDWp7LvmdsrBB8fX8gEy4uTIBKRETAqmfURbOr6xrh6da2MMHbwwVVteqZIWsGHDorfuM26xcbhL/c1w8AMGlwd7i5WLa4/4P/mYAP/mcCJBIJJBIJwgI8RTtCNXTTP7z/n7PYeTQb/9qaZnBeQpS/cO7zM7XTspr8KvrGNFd679sjEHOn9DY5dakh19ldue1ApjbDfYvPIqK2KK+qb/0kog7W2KTE9gOZKCqvbfE8L3cXo99jltBkvY8M8TIr7VJXwZEvHUN6h2L1s2OFReu6vNzlQq6SJ+8xjN7N3Yarm/4hLaMYaRnFcHeVoUlhmFcs2N/DoMB3W4eANeR669aUzPNF1OEu55ZjaJ9QezeDurj9adex/UAmjpzLx1tPjOzQ1+ofF4hFswYyuaoejnzp8fV0NRrpP35XXwT7uePBifEY1s5/PBfPGSzcbm3ESmZksb+xYLA1+te5nFtu4kwiagvdIsKXcsrs1xDqkpQqFTb9chEXs7Xpi/JLakT/7UgSiQR9YwPh6c6xHl0MvsyUHB+Et58ajWkjYto8DKsvJly7PmvayOgWzjTcqTg2OUJUzshc+hn8Tzdn5efEI1H7mtycbBkALuWW2a8h1OXkFFRh6UdH8duJPPzjy5MAgNr6JuzWyTmZX1qDXcdzTF2COghD0RaM6huOw+k30TvK32av2dr0pX6ai0emJ1r1Om6uxte0cdqRqH3dNjQSA3sF48MfzyEjrxy19U0G9WGJOsLLHx8T3b+UU4bvdGr6AsD//t8RAEBdPUsA2RJHvlow69aeGJccgUfvsC7AsYY5wU97tCfYzwPzp/XBq48MN3u9GhFZTiKRIMTfAwmR/lCpgIw8TvFTx7p2s9Kg5BwArPjihMmp7//8ntnBrSJd/PnVAl9PV/zJypEla5kz8DSmfwR2p+bCz6ttifA0ucp0i3Vz4IuoYyRE+eGnI8Cl3HL0iwtq/QlEViivqsern6aYfHxgz2BMGtwdq7YY7rIn22Hw5WDMXU/28vxhHdSAjrksUVfXs7sfJAAuc9E9daCrrSQ1ferevrh2s8pGrSFTON/kIKYMVy/K7RsbaNd2yLjoi6hDeLq7oHuIN67eqEBjk7L1JxBZIbew5cDKRS4TbfLSt/Thoe3dJDKCwZeDmDmxJ9YuHGf3XCjFFUwCSdRREqL80NikxLV8xyguTJ1PgxmBvYtcirULxxl9LCrUu72bREYw+LKDObf1MjgmkUjg5d4+CVTbIjzQ095NIOq0Epp3TnPqkTqKsSThxhjLGQmYnzCc2oa9bAe3Dom0dxOMum1IJHpG+tm7GUSdVq9IfwBMtkodx1gaEw83Gf54ewL+9odBwjH9nJFkW1xwbwcSiUQoNtrWHYvt4cW5Q7ArJQcPTIi3d1OIOrUAHzcA6tJiSpWq1YoWRJb65OcLBsfm3t4bI/uGi46ZGvki22DwZSdjkyOw82g2Rva1f6HRnt390LM7R7yIbKmorBahAZzmp/aTnlUi3I7v5ouwQE/cOy4WwX4eBufqJ+wm22LwZSf3jYtFXIQvBvRkvh+irmREUhiOnsvnqBe1K4VSic2/XoYEwLL5w1rc0Uj2x+DLTlzkMgxt5wLdROT4XJoXNCtVKju3hDqL0xnFOJNRjLzCaoxNjrA68NItAk8di8EXEZENadY5M/ai9nDtZiVWb1Vnq3dzlWHG+DirrzVKb10YdRxudyAisqGaegUAoEnJ6Iva5kxGkaiU0J2jYuDv7WbxdRJjAgAAST0C2q1t1DKOfBER2dDxCwUAgINnbmDmxJ52bg05s+UfHRHdv31YlFXXeWH2QFRUN8DPisCNrMPgi4jIDgpLa+3dBHJQ6ZklOJJ+E3+antjirkQ3VxnqGhTCfRe5zKLXWTR7IBQKFSQSCQMvG2PwRURkB2GBnlAo1aVgmPCSdK38+hQAYEjvUAzsFWzyvNAAT5RXNQAAZk2yfBS1bw/71hLuyviJJyKyobAAdc6lkoo6PP72Xjz+9l77Nogc1q8nclt8PLdAW0R7yvDojm4OtSMGX0RENpTfPN145Fy+nVtCjiileU0gYLxUkK7a+qaObg51EAZfREREDuLU5SLhtoerZWu4yHkw+CIisjMVk35RM93ku2lXilo4k5wZgy8iIjtrUijt3QRyEEqd/G/B/oY1GTUyb1QIt1f+ZUyHtonaH4MvIiIbighSF9OOjdCWgGlsYvBFarprviYPNZ2367V/HxduB/gwTYSzYfBFRGQHmhqPAIMvMk4uY/H1zorBFxGRDWmW9MjlDL5IbOOP50T3zalAFRpgemqSHBeDLyIiG7pZUgMAKK9uEI41cs1Xl6dSqXDw7E3RsfXbzmLZxmMoKBNXQyirqkf3YC8AwCyWqHJKDL6IiOwgr7BauM2RLzpztdjo8dzCKizZcFi0C/Kv6w4ir0j9/gnyc7dJ+6h9MfgiIrKTgT3VpWMYfDm3X1NzUVTetlqdN0tafv43ezMAGKYlyS2sMnY6OTgGX0REdhId5g2AwZcz++1ELr7YdQl/W38YGXnlVl9H0sra+p1Hs7E/7Tqu5VeKjocFeFr9mmQ/DL6IiGwo0FedFmBo7xC4NC+655ov57Xpl0vC7Tc+T+2wnG1e7nJ8/t+LWP7pcdFxTjs6JwZfREQ2dMfIGADA6H4RQroJjnw5J91EpxoKc7YoNmtsUuJidil+ScnBd/uuCsfnTe1tcO4zM/obvbaLnF/jzqjlqp1ERNSuJg6OxKh+4XB3lWPPiVwADL6c1bbfMw2OWVIq6s/v7DV6fMLA7vhs50XRsd7RAejbIwDpWaWi465y1n90Rgy+iIhszN1V/afXpfmLk8GX87mSV44zV4vRO8ofF3PKhONKK/5XPjI9EYkxASiuqEN4kOk1XLHd/ETB19JHRnDky0nx/xoRkZ1wzZfz2va7eprwvvFxouNKK4qkj02OQJCfOxKi/OHr6Wrw+EeLJwIA/L3Fj/WK9rf4tcgxMPgiIrITIfjiyJfDq6ptxJqtabh2sxLHzufjXFYp+sYGIiHKX3SeJdOO5pI2b4UsKK01epycD6cdiYjsRBt8KezcEmrNLynZSMsoxqXcctTWNwEApgw3LHxtwXr7Fr08fxhe/TQFT93bTzimO0I6fWQM/LzdUFjbYOzp5OA48kVEZCfc7eg8NANaDY3aQLl7sLfBec+vPWB0F6SlYsJ98PGSSRjWJ1Q4Nlzn9gMT4tv8GmQ/DL6IiOyEa76cR32DOujSTfdgaorxtX8fN3pclzXTkwlR/nhwQjxee2yExc8lx8Lgi4jITjTBV1FZHf751Ulcu1nZyjPIXnan5hoca8vyrpwCy8sCSSQSTBsZIxTVJufF4IuIyE40wVfKhQKcv1aKd789becWkSX8mncfzp7U0+Cx8uqW12K98klKh7SJnAODLyIiO9Gs+dJQttdqbbIJefP/v9uHR2Ns/wjRY6//23RwVVJR16HtIsfH4IuIyE6YILPzkOr9ryyuqDd5roQpIro8sz75aWlpmDt3LgDg2rVr+MMf/oA5c+bg5ZdfhrI5ne+6devwwAMPYPbs2Th9+rTF5xIRdTUMvpyDOYlTT10uMuuc9KwS0bH7xsVa3S5yXq1+8j/88EO89NJLqK9XR/FvvfUWFi5ciC+//BIqlQq//vor0tPTcezYMWzduhWrVq3Cq6++avG5RERdDYMv51BSbjhNOEtvnVdFTWOr13n329NYufmUsNNxZFIY7hrD4KsravWTHx0djbVr1wr309PTMXz4cADA+PHjcejQIaSmpmLs2LGQSCTo1q0bFAoFSkpKLDqXiKirkcsYfDmDGyU1AAC5TDtdOGV4tEXXUOgUfdQMpHH6setqNcP9lClTkJur3WKrUqmEN4yXlxcqKytRVVUFf39/4RzNcUvODQwMbLEdAQGekLN6O0JCfOzdBKfEfrMO+80y1vSXi1wqJFktr27osn3uyP/uyvMFAIA7x8Zh274MAIbtnTc9EZ/9dB7z70jCpzvOAQBultejf89gAEBZpXYN2LaDWQAAlaTt/25H7jdHZ8++s7i8kFRnVWF1dTV8fX3h7e2N6upq0XEfHx+Lzm1NaWmNpU3tdEJCfFBYyDxAlmK/WYf9Zhlr+0suk4oy3HfFPnf091r6lUIAgKtUO1Kl394JyRGYkByBlAsFwrEX1x/EhkW3wNVFhrwi7ffewdPXAQBHzt5s07/b0fvNkdmi71oK7iwe805KSsLRo0cBAPv378fQoUMxePBgHDhwAEqlEtevX4dSqURgYKBF5xIRdUVc9+X4DqfnAwC27r3S6rkx4eIv3CaFeo6xqoY1GEnL4pGvxYsXY+nSpVi1ahXi4uIwZcoUyGQyDB06FLNmzYJSqcSyZcssPpeIqCvSz/VFjisqxBvZrWSm93YXf63uPp6Du8b0QKWRBfm3Dols1/aR8zAr+IqMjMSWLVsAALGxsdi0aZPBOQsWLMCCBQtExyw5l4ioK+LIl2OrrW8SbveJCUCfmAAMbF7HZYz+IvptBzJRUlmPqFDDItwyKRfcd1UWj3wREVH70Q++auub4OHGP82O4i//2i/cDvJ1x+RhUS2eL9ULqGLCfLA/7brRc39JycHsW3u1vZHkdPiTi4jIjvSDr65Uembzr5fx3f6r9m6G2Ub0DWv1HN10FBIJsPihQR3ZJHJSDL6IiOxIf81XSaXpsjSdzS8pOfjxUJaQdNTRyfVrCBkh0zlHKpHA3VWOaSMtywlGnR+DLyIiO9KfpupKI18aN4udJZWQeUFi92AvAOqRLwBwZY5K0sPgi4jIjvSTnLdUkLmzuphdau8mGKU/IiczY+QLADyEHY/q/7muJjZVDOpleuE+dW4MvoiI7Eh/v1tpFxz5uuSwwZf4vpureSNYXs0bJpoU6uS5ciPB18ikMPxpemLbGkhOi8EXEZEdpWeJA4/iLhJ86dY6vHTNMYMvhdK6tWieerm+9DdV3Do4Ek/c3RfeHi5Wt42cG4MvIiIH0lUW3Dc0aoOvjLxyUYklR6EbIFrC010cVOlPO04e3nK6Cur8GHwRETmQkop6p9n91xa6yUubFErktJI53h6UOiNf/3pmjNnP83TTH/kST1fyi5f4HiAiciBNCqXRUjSdjW7wBQAZ18vt1BLTNNOOQ3qHwM/bzezneelNOyr1pi/1d7hS18Pgi4jIwZRUdv51X/WN4im9zOsVdmqJaZrgy9IyQB56wZd+sKVfgoi6HgZfREQOpri886/70owOubvK4O3hgqsOGHw1NK9DszT48nQTr/mKCPQU3dcfGaOuh8EXEZGD6QojX+XVDQCASYMjkRAdgIKyWlTWNNi5VWKXsssAAFGhPhY9z8NNvMYryM9duL124Ti4ujDpalfH8JuIyMF0hSz3Fc3Bl6+XK3x93HHiYgEyb1QgOd5xEo+ezigCACTHB1n0PHdX8Verh5scL80bikBfN3i5M70EceSLiMjhlHSBLPcVNZrgywW9YwIAwKGmHpsUSqRnlSDYzx0RQZ6tP0GHfp4vAIjr5gt/CxbtU+fG4IuIyIHIpJIuMe2oGfny83JDryh/AI4VfF3OLUdtvQID4oMtXiDPNV3UGr5DiIgciL+3W5cY+covrQWgnnb083ZDqL8HMm9UQKlSQeoAuwGFKceelk05AoCPpyvuHtMDPSJ827tZ1Elw5IuIyIEE+bqhrKpeqAvYGdU3KnD0XD4AwM/LFYB6Wq66rgn5JTX2bJrgdEYxXOVS9In2t+r5946Lw8CejrN+jRwLgy8iIgcS6OsOlQooq+q8o1+6uxo166Niu6lHiRxh6rGwrBY3imuQGBNgkJ2eqD0w+CIiciABvupF2V1h6hGAMMUY380PAHD1hv2Dr9MZxQCAZI5cUQdh8EVE5ECCfNU5obrContdUaHekMskDjHylaZZ7xVn+XovInMw+CIichASAIE+6uCruLzzBl/GFtS7yKWIDvNBbkEVGhoVZl2nsUmBc1klBrUT2yK/tAZnr5YgMsRLlByVqD0x+CIiciCBzdOO3+676nAZ39uLJru9vrgIXyiUKmTnV5l1ndc/S8U7m09h24Gr7dKuA6dv4H//7wgAoFuwV7tck8gYBl9ERA4k0Fc72pJ5o9KOLek4ZzNLjB6Pa150n3KhwKzr5BSog7QfD11rl3Z9/NN54XZCc+4xoo7A4IuIyFFIxAk6XeSd80+0fqFpDU3wtet4ji2bAwCobxBPdXbnyBd1ICZZJSJyILrZ1OUy+ycb7QgXskuNHg/x9zDr+d8fzERYgGUlf1pz/pq4TV4erMFIHYfBFxGRg5BAHGydzypFr0h/+zSmg1TVNuK3E3kAgFi9DPASiQRurjLIpRKczig2WdB62++Z7d4u/XqMPp6u7f4aRBqdc0ybiMhJhAVoR3v0NwHKOuHI15Y9V4TbwUZ2E0olQHVdE1ZvTUNhWa3N2qW/Y1KTeZ+oIzD4IiKyo1mTepl8zBFqHLYnlUqFA6dvCPfnTultcI7u6N9/9rfPLkZz6I98EXUkBl9ERHakO7o1bkA30WNVtY22bk6H+vM7+0T3vY2sq6qpbxJuHzmXj+tF1R3eLgBQqtovVxhRaxh8ERHZkVSqDb70CzH/fDTb1s0xW5NCiUs5ZcJ0XZNC2WoxcGuKhb/00VGs2JSKv60/ZFU7zaXQmXacMT6uQ1+LiOOsRER2JGtlavHY+XwMTwyzUWvM9+EP54R8XPOn9cGnP18AAGxYdAtcXWRobFKivlFhdHTLUpdyywGopy1vltQIxyUAVABcXaRQqVSinaKWKK2sxxufpQIA7hzdA3eO7tHGFhO1jCNfRER21Nqi+h8PZdmmIRbSTYSqCbwAID1LnUD1qZX78Oya39u19E9eUTU2/XJJuK+5ckOjEtV1TcafZIafj2qTtA7oyXqO1PEYfBER2ZHutKMxZVWOXWJoUK9gPDQ5Qbhf31yXUbOGSmEi+LptSKTFr7V1T4ZBPi6Nkgrra2He0FlXFt/Nz+rrEJmLwRcRkR3Jpdo/w8ZmzRxp0X2TQokvfrmE7Hxt2aNAX3fcqhNI5RWKF8g3NBkvkq0ZIbPEmavFcJFLjWb+f/vLkxZfT6NJwcX2ZFsMvoiI7Ki1kS9HcujsTfx6IhevfJIiHNNfRN/YJL7/3ndnoFSqkKe3a3HaiBir2nDHqBijdRd1d0laqklp+UYAorZg8EVEZEcyI8FXdKi3HVrSuiPpNw2OFetN9+mP1F3ILkNaRhG2Nefs6hHug3HJERjVz/gmgtYW6E8bEW0Q4LVVUxNHvsi2GHwREdmRbvCluTW6X7h9GtOK5Phgg2Nnr4qnD3tH++PgmRuiY98fyELqpULEd/PF0oeH4k/TEyGTGv/6efOJkS22wUUuw6WcMssa3gqOfJGtMfgiIrIjo9OODprZ3sfT9KhUfDd1ncbauiZs3HFe9Ni15jViM26JbzUdhLeHi9HRQABYNHugBa01n4JrvsjGGHwREdmRsUDDMUOvlmPCu8b0AABcLzaekT6pRwASYwLMep0x/SMMjr3x+Aj07REIABir87jubWs3J2h2aBLZCoMvIiI7EgVfjhp1NWspl5aXu3pUrLDMeMqHGePjzX6dWZN6GhyLCPISbk8bGS3cHpSgnQp9ds3vKC43/vrHLxQg80aFwfEmhRKllfVmt42oPTD4IiKyI5nMyJ9hBw3Cvtp92eRjXs0L5U0FP3HN05Lm8HCTo2d3bb6tP03rI3o8LMBTuK0/jZlTUGX0mu9vO4vX/n3c4PiLHxwRbjvqWjvqfBh8ERHZkdTIXF7alSKbt6OipgGPv70Hv6bmWvQ8Tzd1lTpPd/V/C8tqDc5pS1CT1CPAoOC47jo5/cLbZVWGo1jlRo5pjhfpBIvx3ZlglWyDwRcRkR3plheSNA95ncsynsW9I529WgyFUoUvdl3Ct/syoFJpF6ErlKan5jR1EL2agy9jS9e7BXsZOdqyOZN7IdjPHTMnGk5B6jqdUSy6/9l/L+K7/Rmo0Zki1Z0K1QSXNXVN+NeWNNFz25Iln8gSDL6IiOzI1M4+W5PozHXuOHwN+aXaEaxXPzmORe8dNPq8cQPUC95lUincXWWix1Y8OQrTRkRbVUqoR7gv3n5qNKLDfFo8TyoB5t6eIDr246FreGb1fqzacgoKpRJvbkoVHvti1yU0Nimw7rvTyNabotxx+BqIbIHBFxGRHZmT4b62DdnbrbXvVJ6QzDS30Pg6qlcfGS4stAcgug0Aof4eeHBiT7i6yPSf2m4uZJdh4mBtcPfmEyMxdbh6Qf7ZqyX44PtzBs/54PtzuJBdhiEJIR3WLqKWMPgiIrIj0Zqv5ptP3JUkOuf1zwwXirc7vRjwv8dy8Nd1B/DZzgsmnyKXiZ/k5SHviJa1KDk+SHQ/0McNU3V2Q17INpzCTb1UiD7R/nji7iSDx4hswargq6GhAYsWLcLMmTPxyCOPICsrC6dOncKDDz6I2bNnY926dQAApVKJZcuWYdasWZg7dy6uXVMP6Ro7l4iI1Eb2DRdN4d0orjHreSqVCvUN1uWs0l+43i8uEHK5FHtPXReORYeJyx656Y1oZecbHyHrSL316jy6yKWiYuWVNYa5vyQAnpmRDBe5uP0RQZ4G5xJ1BKt+pmzZsgWenp7YsmULrl69itdeew1FRUVYu3YtoqKi8MQTT+DcuXPIzc1FQ0MDvv76a5w6dQorVqzA+vXr8fLLLxucm5TEXyBERBpymRSAZYHUpz9fwO+nb+Dtp0Yh2M/DoueWVzWI7s+a1AsRgZ44d60Eq75WL0yvaSHPl61NGxGNn49m4/bhUQCA954fj9r6JkgkEoMROX3/+8chwu5MXb0i/TuiqUQGrBr5unLlCsaPHw8AiIuLw5kzZ9DQ0IDo6GhIJBKMHTsWhw4dQmpqKsaNGwcAGDhwIM6ePYuqqiqj5xIRUdv8flpdUzHzRmWbryWTSiCVStAvVjut5+/jJjpHZceqPA9O7ImPl0wSakR6uMkR6OsOQBO4mhbo62b0uIdbx61NI9Jl1chXYmIi9uzZg9tuuw1paWmorKxEVFSU8LiXlxdycnJQVVUFb2/tMLVMJjM4pjm3NQEBnpDL+cEICWl55w8Zx36zDvvNMm3trwA/T+Eawf4eonI5llzb29vN4ra46y2WDwn2RkiQOEXEtNGxWLvllHA/IS64xQ0DlrShPd9rqlaiwpAQHyFQ0xUa5O1073lna68jsWffWRV83X///cjIyMCcOXMwePBg9OnTB7W12m3J1dXV8PX1RV1dHaqrtesIlEolvL29Rcc057amtNS8NQ+dWUiIDwoL2/6Ltqthv1mH/WaZ9uivsvIa4Rp/nJwgLLR3lUstuvblrBIkRlqWMFSuF0M11jWgsFApOlZfK56aLC5ueY2XuW229Xstv6ACinp1YLvkocHYuOMc4rv7YXRiqFO95/kZtZ4t+q6l4M6qacczZ85g1KhR+OqrrzB16lT06NEDLi4uyM7OhkqlwoEDBzB06FAMHjwY+/fvB6BeZJ+QkABvb2+j5xIRkZa/t6twOzTAsoXgJVbUKtQv/+Puavjb3J7TjO1J99+WEOWPfzw5Gk/c1RdurpxdIduwauQrJiYGa9aswYYNG+Dj44M33ngDN27cwAsvvACFQoGxY8diwIAB6N+/Pw4ePIjZs2dDpVLhzTffBAC8+uqrBucSEXV1pibwQvwNp8ha4urSMVmEzMlJptE/Lqj1k+zE28Ol9ZOIOpBVwVdgYCA+/fRT0bGwsDBs2bJFdEwqlWL58uUGzx84cKDBuUREpKVbMNrSLPgurSw4t9aAnuYHVAsfTO6QNlhrTP9wzJ/Wp9OM3pFzY5JVIiIHVN+oTTNhacBg6fl1DU14f9tZk497NBfPbm0X4f/+cTAA4O9zh4iCR0fwyPREyKTSVv8NRLZg+3TERERknE7AointAxgvVt0ShdKyZ2z7PbPFx1cvGIsmhbLFcwB1nqyPl0yy6LVtoU+0v8MFg9S18ScAEZED0s051VrqBH1KC4OvHL0C03eN7iG67yKXCqNfziipR6C9m0AkwuCLiMgBBft54OGpvQGopxHLq+qxfttZ5Je0nnZHYWGw5q63yy/ARBJSmVSCYD/LFv87gluHRLZ+EpENOe9PGSKiTkZ/Ymx4Yhj+vfMiAOCbvRlIuVCAovJaLH14mMFzdacFlcrWpwh16U9TBvoYD74kEgnefmo0jqTfRLC/ZeWL7OXtp0Y59agddU58RxIROQhT04tNCiXqmgtmNzQaD6zqdApq19RbVhNSfz1XgE/Lo1sj+4ZbdH17CjARSBLZE4MvIiIHYWq68GxmCQb2DAYA1DYYL25dV689fvxCgUWv26QQv25nCFg+WjwRTU1KofYjkSPhu5KIyEHoL5TXHQk7daUIAFBSYTx7ve7Il6UUetOUXu7O/7tcKpHA1YUZ68kxMfgiInIQ+muvLNm0+OuJXKtft6lJ/EJMy0DUsRh8ERE5CP2RL1e5+X+i9526bvXrNlm4QJ+I2obBFxGRg9Af+WqPaTOlSoXvD2TiRnG1yXP013wRUcdi8EVE5CAsTY5qjrTLRdh2IBOvfJJi8pymJo58EdkSgy8iIgdhaVkgc1TVNQIQlyvSV1PfKNzuH2d+8Wwisg6DLyIiB9ERI1/mFIas1ckL9vzMAe3fBiISYfBFROQgLC0L1Jqi8lqczSxp12sSUds5fzIXIqJOQtGOC98LSmuw5P+OtNv1iKj9cOSLiMhBmDvt2NDYekLVc1mlZr/uQ5MTAAAzJ/Y0+zlEZD0GX0REDsLcBfdVtY2tnpOeZf50o0tzPjFfLxezn0NE1mPwRUTkIEwV1tZnTvB14ZrhyJep62tG3KTMbE9kEwy+iIicjDnBV3WdYQHunIIqo+dmXC8HABSW1batYURkFgZfRESOwsyBJ3OCL2POXC02evx6UQ0AoLTSeNFuImpfDL6IiJxMdSvB1/xpfYzGcZU1xp83IikMANA3lglWiWyBwRcRkZNpaeRryvAojB/QDdFhPgaPmVpSJqz54jcCkU3wo0ZE5GSqag3Xc2mEBXoCAJJiAwweU5lId685LuGCeyKbYPBFROQgJGYu+mpp5EuTqLVvj0CDx1od+WLwRWQTDL6IiJxMdZ3p4KtJoS6g3SvSz+AxU6kmNEW3Oe1IZBv8qBER2dltQyMBAIkxhlOFxuiPfH2x65Jw28/LFQDgIpcZPM/UyNf3B7MAALkF1Wa9PhG1DWs7EhHZ2R9u7YW7x8TC28O8DPP6wdevqbnCbd/m4MuY1pK41tRbl8KCiCzDkS8iIjuTSCRmB14AUGUiZQTQcqowY9WLyqq0ub1cjYyWEVH7Y/BFROTA3FwNA6Ka+iYolEqj57e8Y9Ew+jqnUwNybHKExe0jIssx+CIicmCDewUbPW6sfBAAuLiY/rNubOTrbKY6+Fr+yHD4e7tZ3kAishiDLyIiB2Zs4TxgOst9XISvyWuVVzWI7itVKpzLLIGflyu6h3hZ30gisgiDLyIiB+YqN/5n2lSur5amHfVrO+YWVKGiphF9YwOZYJXIhhh8ERE5MFPTiMaCryBfd4uund485dg31jAhKxF1HAZfREQOzNQORGPB1z+fHi2679I8ahbq72H0Gpr1Xsay4RNRx2HwRUTkwFz0ph3dm3c/VrdQ31FDM2UZ1027DkyzS7K+UYHLuWWIDvNuMTcYEbU/Bl9ERA5MP/jy9VQHSi3Vd9SYPCwKADCyb7hw7Eh6PgDgYnYZmhQqTjkS2QEz3BMROTD9Bfc+ni4oKKtFVW2DiWdo3T0mFpMGR4oSuG7ccR4bd5zH0D6hAIB+nHIksjmOfBEROTD9NV8+wshX69OOAExmzj9+oQCucil6Rvq3qX1EZDkGX0REDkx/2tHLQw4JgBOXCvHiB0egbKVeY0t6RwcYXJ+IOh4/dUREDsxYcOTprl4xcrOkBvUNCrOu0zvKH2P6h4uOcb0XkX1wzRcRkQPTX/PVs7sfLueWC+WFzB34WvzQYADAwTM3hWP9GHwR2QVHvoiIHJiLi3bNV0KkH8b0j9Bbx2X9tGNEkGcbWkZE1mLwRUTkwHRHvh69MwlymVQUfFkferVcioiIOg6DLyIiB6a75kvaHCyJgq+2RF9EZBcMvoiIHJhu8OXnrU4zYSp9hCVcTdSMJKKOx08fEZED083zJZep/2R7iUa+LBv66hPtDwCIDvVpe+OIyCpW7XZsbGzEkiVLkJeXB6lUitdeew1yuRxLliyBRCJBr1698PLLL0MqlWLdunXYu3cv5HI5XnzxRSQnJ+PatWtGzyUiIjFjqSbasubr7jGxuJB9EneP6dG2hhGR1ayKePbt24empiZs3rwZf/nLX7B69Wq89dZbWLhwIb788kuoVCr8+uuvSE9Px7Fjx7B161asWrUKr776KgAYPZeIiAwZmx4UTTtaGH31iQnAxsUT0S8uqI0tIyJrWRV8xcbGQqFQQKlUoqqqCnK5HOnp6Rg+fDgAYPz48Th06BBSU1MxduxYSCQSdOvWDQqFAiUlJUbPJSIiQzIjswLe7tpJi+z8SouvyV2ORPZl1bSjp6cn8vLyMG3aNJSWlmLDhg1ISUkRPtBeXl6orKxEVVUV/P39hedpjqtUKoNzWxMQ4Am5Xo2zrigkhOs0rMF+sw77zTId3V+a61c1KoVjq7ak2ez1O5Izt92e2G/Ws2ffWRV8ffrppxg7diwWLVqEGzdu4OGHH0ZjY6PweHV1NXx9feHt7Y3q6mrRcR8fH9H6Ls25rSktrbGmqZ1KSIgPCgst/5Xb1bHfrMN+s4wt+ktz/ca6xhYfdzZ8r1mH/WY9W/RdS8GdVdOOvr6+8PFRX9TPzw9NTU1ISkrC0aNHAQD79+/H0KFDMXjwYBw4cABKpRLXr1+HUqlEYGCg0XOJiMg83h6sDEfkzKz6BM+fPx8vvvgi5syZg8bGRjz//PPo168fli5dilWrViEuLg5TpkyBTCbD0KFDMWvWLCiVSixbtgwAsHjxYoNziYjIPC5cgkHk1KwKvry8vLBmzRqD45s2bTI4tmDBAixYsEB0LDY21ui5RERERJ0dk2sRERER2RCDLyIiIiIbYvBFROSE4ru3vkuciBwTgy8iIidUUlFv7yYQkZUYfBEROaHSSgZfRM6KyWKIiBzc9JExqDaRWJWInA+DLyIiB/fAhHh7N4GI2hGnHYmIiIhsiMEXERERkQ0x+CIi6gQSYwLs3QQiMhODLyIiJ+fv7Yq/zhpg72YQkZkYfBERObnwQE/IpPxzTuQs+GklIiIisiEGX0REREQ2xOCLiMgJDewZbO8mEJGVGHwRETmhRoXS3k0gIisx+CIickIlFXXCbYlEYseWEJGlGHwRETmhG8U19m4CEVmJwRcRERGRDTH4IiJyQs/M6G/vJhCRlRh8ERE5ocEJIfjT9D4AgLHJEXZuDRFZQm7vBhARkXXGJXfDkIRQeLrzTzmRM+HIFxGRE2PgReR8GHwRERER2RCDLyIiIiIbYvBFREREZEMMvoiIiIhsiMEXERERkQ0x+CIiIiKyIQZfRERERDbE4IuIiIjIhhh8EREREdkQgy8iIiIiG2LwRURERGRDEpVKpbJ3I4iIiIi6Co58EREREdkQgy8iIiIiG2LwRURERGRDDL6IiIiIbIjBFxEREZENMfgiIiIisiEGXw6KGUDIVvhesw77zXyNjY24fv26vZvhdBobG5GSkmLvZnQKjvZ5ZfDlILZs2YIVK1Zg27ZtAACJRGLfBjkBzYcpLS0Nubm5dm6Nc7ly5QpeeeUVAHyvmWvz5s145ZVXsHXrVgDsN3Nt27YNc+fOxd69e+3dFKeye/du3HvvvcLnlCzj6N+pDL7sSKlUQqVSYcOGDdi3bx/uu+8+7Ny5E5988om9m+YUNB+m5cuX4/Dhw6ivr7dzixyb7i+/K1euYOvWrTh+/DgA9XuRDGn6ZdOmTThw4ABmzJiB7du346uvvgLgeL+mHYVSqURDQwOWL1+Oo0eP4sMPP8ScOXOE/mK/mXb9+nU8/fTT2LlzJx588EHce++9AICmpib7NswJONN3KoMvO6mqqoJKpYJEIkFhYSEmTZqE3r17Y9GiRfj4449x8eJFezfRYdXV1Ql/iH788UfcvHkTJ06cQEZGhp1b5riqqqqE2wUFBUhJScHs2bOFX9VSKf8U6NPtsytXrmDSpElITk7GLbfcAqlUisbGRof7Ne0INP3m6uoKuVwOX19ffPfdd3jsscfw9NNP4+rVq+w3IzT91tTUhPnz5+Odd95BcnIyjhw5AgCQy+X2bJ7Dc7bvVP7FtYP3338fCxcuxLvvvouUlBRER0ejsrISDQ0N6NWrF2JiYrBnzx4A/IWoLy8vD++88w5OnToFAOjevTs2bdqEoKAgpKSkoKyszK7tc0Sa99vq1auRnp6O0NBQjB8/HkuXLkVAQAA+/vhjABz90qXpszVr1iAtLQ2PP/447r33XqSkpODjjz9Gamoqli1bBoVCYe+mOhRNv61atQoZGRl44okn8Msvv6CyshIfffQREhMT8eWXX6K6utreTXUo77//Pp577jmsWbMGZWVlGD58OAD1mq9BgwYB4OezJc74ncrgy8aOHTuG9PR0vPXWWwgKCsK+fftQXl6O0tJSLFmyBI8++igmT56MvXv3oqioiL8Q9Zw4cQJ79+5FWloaKisrER8fj9jYWNx+++04d+4crly5Yu8mOhTd91tISAi++eYbpKam4pZbbgEAvPjii9i0aRNqamoglUod5g+TPen2WXBwMLZv346cnBxIpVLExcVh586dePvtt3HmzBmH+zVtT7r9Fh4ejs8//1xYWzh9+nQAwLPPPoujR4+ioKDAzq11HJp+W7FiBYKCgrBt2zYcPnwYgDrg+v333wFwdNoUZ/1O5f9NGzt79iyGDh2KkJAQTJ8+HT179kRhYSGefvppTJs2DQsXLsRdd92FoUOHIjg42N7NdTh5eXmYPn06qqqqkJKSAl9fXwBAcnIy4uLisG/fPu6q0qH7fps2bRr69u2Lbdu2Cb+iExMTMWjQICxZsgSA4y1KtQf9z2hSUhJ27NgBAFAoFJDJZCgsLETPnj3h4eFh59Y6Dv33Wv/+/bFjxw6MGTMGLi4uuHnzJkpKSpCYmCh8bsmw35KSkvDTTz9BqVRi1KhRCA8Px+7duwE4zqiNI3HW71QGXzai+bJLSkoSFusGBwejX79+cHNzw5kzZ+Dv74/PPvsMTz/9NHr16mXP5jokpVKJu+66C8888wx8fHxw7tw55OTkCI/fcccdqKystGMLHYex91tQUBAGDhwIDw8PYdoWUG9YmDp1qj2a6VBa6jNPT0/s27cPv/32G5577jksXLgQEyZMQGxsrD2b7BBM9duAAQPg6emJgwcP4qeffsKiRYvw9NNPY/To0QgKCrJnkx1Ca5/RkydPAgCGDBmCCxcuCOuZSM3Zv1O5gq+DfPPNN3Bzc8PQoUMREREBlUoFpVKJkSNHolevXvjXv/6F559/HnFxcaivr4ePjw969+6N2tpajB49uksvrtTvO81og1QqRUREBKRSKcaMGYMffvgBJ0+eRFRUFAAgMjISL7/8cpf8A7V161a4uLhg7NixCA4ObvH9VltbCy8vLwDqkRwvLy9hWqgrsbTPwsPDccstt6Bfv37o06dPl/2MWttvkyZNQlxcHGQymb3/CXZhSb/V1NTA29sbADBp0iR069atS/5d02VJ/znDdypHvtpZaWkpHn74YaSlpaGoqAjvvfcejh8/LgQPV65cwbx587Bjxw4cPnwYBw8exLVr19DY2AgAGD9+vMO9SWylpb4D1Ou98vLyAAC9e/dGt27dcPbsWdHoV1f6A6VSqVBeXo7HH38caWlpyMzMxLp163DixIlW32+aheJd7YvQ2j7LysoSPqP9+vXrcp/R9ui3Xr168f1mZr9lZ2cLO7ojIyO77HqvtvyNc/jvVBW1q4yMDNXSpUuF+5s2bVItWLBAVV1drXr11VdVs2fPVtXV1al27dqlev/991UPP/yw6vDhw3ZsseNore/++Mc/qrKzs4XHi4uLVVlZWfZoqt3V19erVCqVqqSkRLV8+XKVSqVSNTU1qb755hvVk08+yfebEewz67DfrMN+a5vO3n8SlYor+Kylap6D37x5Mzw8PHDPPffg8OHDWLNmDT7//HO4uLhg+/bt+P777zFq1CiMHDkS/fr1s3ezHQL7zjp1dXV455130NDQgEGDBmHw4MFYvnw53nvvPbi7u6O6uhrLly9HTEwMJkyYgKSkJHs32e7YZ9Zhv1mH/dY2XaX/uuZYZjvRTHEdOnQIH3zwgbA7JTg4GG+++SZWr16NAwcOYNSoUXB3dxeCB+YGYt9Zo6ysDC+99BL8/Pwwd+5crFixAm5ubvDx8cGmTZsAAG5ubhg5ciRkMpnwR4l9xj6zFPvNOuy3tulK/cfgywqFhYXC7ZSUFAQEBCA8PBzLly8HALzyyiuYOXMm5HI5li1bBg8PD4SEhAjP6WrrHnSx7yyn6TOlUonS0lI89NBD6NWrF6ZOnYr09HQsWLAAP/zwAy5dugS5XI7r168jICBAeD77jH1mLvabddhvbdMV+88BV6E5rps3b2Lt2rUoLi7GpEmTMH78eMTFxWHevHno1q0bJk+ejEcffRRRUVEoKytDZGQk3nzzTRQWFuKFF16wd/Ptin1nOd0+mzx5MuLj4/H3v/9dyJFUWFiIkJAQxMfH4+6778aWLVuQk5ODxsZGPPfcc3ZuvX2wz6zDfrMO+61tunL/cc2XBd5//300NjYKxXVLS0vx17/+Vdi2v3r1aly8eBHr169HY2Mjrl69ijNnzuCBBx6wc8vtj31nOd0++/7771FcXIxFixbBy8sLx48fx/r167Fx40YAQHl5OWQyGY4fP44JEybYt+F2xD6zDvvNOuy3tunK/cfgqxXffvstjh07hqioKOTl5eHpp59GVFQUrl27hq+//hphYWF4+OGHhfOHDx+OFStWYNKkSXZstWNg31nOVJ9lZ2dj8+bNCA0Nxfz58/HNN99AKpUiKCgIa9euxYIFC4SSQV0N+8w67DfrsN/ahv2nxjVfLXjnnXewf/9+zJs3DxcvXsR//vMfbN68GQAQHh6O0aNH4/r166JizqtWrRKSfnZl7DvLtdRnYWFhQp8BwK5du/DGG2/gl19+wSuvvNKp/ihZgn1mHfabddhvbcP+0+KarxZUVlZi1qxZ6Nu3Lx566CGEhobixx9/xJ133onExEQEBQWhvr4enp6eQuqEsWPH2rvZDoF9Zzlz+qyurg6VlZXo378/ZsyYgSlTpti72XbFPrMO+8067Le2Yf9pMfgyQalU4vbbb0dycjIA4KeffsKtt96KhIQEvPHGG3jttddw6NAhlJWVQalUdqnM6q1h31nOkj7z9PTEM888Y+cW2x/7zDrsN+uw39qG/SfGNV9mqKqqwvz587F+/XqEhIRg/fr1KC8vR1FRERYvXixKhUBi7DvLsc8sxz6zDvvNOuy3tmH/ceTLLPn5+Rg9ejQqKyvx+uuvo1evXli0aBFcXFzs3TSHx76zHPvMcuwz67DfrMN+axv2H4Mvs6SkpOCDDz5Aeno67rnnHtx99932bpLTYN9Zjn1mOfaZddhv1mG/tQ37j9OOZvn2229RWFiIRx55BK6urvZujlNh31mOfWY59pl12G/WYb+1DfuPwZdZNLvxyHLsO8uxzyzHPrMO+8067Le2Yf8x+CIiIiKyKSZZJSIiIrIhBl9ERERENsTgi4iIiMiGGHwRERER2RDzfBGRw8rNzcXUqVMRHx8PAKirq0Pv3r2xbNkyBAcHm3ze3Llz8fnnn5v9OuvXr8fOnTsBABcuXECfPn0AAFOnTkVubi5mz56N/v37t+FfQkSkxd2OROSwcnNzMW/ePPz2228A1FvUV61ahdTUVHz55Zcmn9e7d29cvHjRqtdsy3OJiMzBkS8ichoSiQQLFizAmDFjcOHCBWzatAmXL19GUVERYmNjsW7dOrzzzjsAgAcffBBbt27F/v378e6776KpqQmRkZF47bXXEBAQYPZrzp07Vyjyu2HDBqhUKmRnZ2PKlCnw8fHB7t27AQAffPABgoOD2/x6RNT5cc0XETkVV1dXxMTEYPfu3XBxccHXX3+NXbt2ob6+Hvv27cNLL70EANi6dStKSkqwcuVKbNy4Edu2bcPYsWOF4MwaaWlpeOutt7Bjxw5s3rwZgYGB+O6779C7d2/s2LGj3V+PiDonjnwRkdORSCRISkpCVFQUvvjiC1y9ehVZWVmoqakRnZeWloYbN25g3rx5AAClUgk/Pz+rXzchIQEREREAgICAAIwaNQoA0K1bN1RUVLT76xFR58Tgi4icSkNDAzIzM5GTk4M1a9Zg3rx5mDFjBkpLS6G/hFWhUGDw4MHYsGEDAKC+vh7V1dU4c+aMMELWr18/vPHGG2a9touLi+i+TCYz6/WIiHRx2pGInIZSqcTatWsxYMAA5OTkYNq0abj//vsRHByMlJQUKBQKAOqgqKmpCQMGDMCpU6eQmZkJAHj//ffx9ttvo3///ti+fTu2b99uduBlDlOvR0SkiyNfROTQCgoKcM899wBQB1+JiYlYuXIl8vPz8cILL2Dnzp1wdXXFwIEDkZubCwC49dZbcc899+C7777Dm2++iYULF0KpVCIsLAz//Oc/O6ytISEhNn09InJOTDVBREREZEOcdiQiIiKyIQZfRERERDbE4IuIiIjIhhh8EREREdkQgy8iIiIiG2LwRURERGRDDL6IiIiIbIjBFxEREZEN/T94/6raOmx4mQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resam['Price'].plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "791b8ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# resam.to_csv('nif_refinitiv_resampled.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cc22f60",
   "metadata": {},
   "source": [
    "## 二、Market Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e9724740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:16:00+05:30</th>\n",
       "      <td>11078.30</td>\n",
       "      <td>216150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:17:00+05:30</th>\n",
       "      <td>11064.55</td>\n",
       "      <td>139350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:18:00+05:30</th>\n",
       "      <td>11070.40</td>\n",
       "      <td>127950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:19:00+05:30</th>\n",
       "      <td>11078.05</td>\n",
       "      <td>84000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:20:00+05:30</th>\n",
       "      <td>11082.50</td>\n",
       "      <td>104700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price    Volume\n",
       "Date-Time                                    \n",
       "2019-08-01 09:16:00+05:30  11078.30  216150.0\n",
       "2019-08-01 09:17:00+05:30  11064.55  139350.0\n",
       "2019-08-01 09:18:00+05:30  11070.40  127950.0\n",
       "2019-08-01 09:19:00+05:30  11078.05   84000.0\n",
       "2019-08-01 09:20:00+05:30  11082.50  104700.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resam_data = pd.read_csv('./nif_refinitiv_resampled.csv', index_col=0, parse_dates=True).iloc[:]\n",
    "resam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0e15848a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 99944 entries, 2019-08-01 09:16:00+05:30 to 2020-08-31 15:31:00+05:30\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Price   99944 non-null  float64\n",
      " 1   Volume  99944 non-null  float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 2.3 MB\n"
     ]
    }
   ],
   "source": [
    "resam_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d1477fb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date-Time'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAFjCAYAAAAD725WAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABez0lEQVR4nO3dd2CTdf4H8HdG994tdNAWCi1Q9h4Cigw3KnB4IOc69UTx8Hfw8wQVF+cJB4LCT0U9RUVQD1SUE5QhuxQoUHZp6QC6926S3x9pnuTJaJO0zWjfr39Mnjx58uVr0nzyHZ+PRKVSqUBERERENiG1dwOIiIiIuhIGX0REREQ2xOCLiIiIyIYYfBERERHZEIMvIiIiIhti8EVERERkQ3J7N8BchYWV9m6C3QUEeKK0tMbezXA67DfrsN8sw/6yHvvOOuw369mi70JCfEw+xpEvJyKXy+zdBKfEfrMO+80y7C/rse+sw36znr37jsEXERERkQ0x+CIiIiKyIQZfRERERDbE4IuIiIjIhhh8EREREdkQgy8iIiIiG2LwRURERGRDDL6IiIiIbIjBFxEREZENMfgiIiIisiGnqe1IRM7ldEYxbpbUYHS/cJy4VAiVSoWEKH9EBHlBoVQiI68C8d19IZPyNyARdS0MvoioQ6zemgYA2PzrZdHxeVN747OdFwEAg3oFY8H9yTZvGxGRPTH4IqJ2pVSpcD6r1OTjmsALAE5eLrJFk4iIHArH+4lIUNfQJLqvUqnMet6u4zm4lFMGAPh2bwZWfn3K5Lmzb+1lbfOIiDoFBl9EXViTQomislrU1jfh2s1KPL1qP9Y0TxfWNTTh0X/swXf7r7Z4jfoGBb7afRkrvjiBJoUSPx/NNnmuTCrB7cOiIJVI2vXfQUTkTDjtSNSFPfHPvQbH0jKKUd+owKULBQCAHw9lYcb4OJPXqKnXjpat3HzK5Hl+3q6YN6U3AMDdVSZ6HhFRV8Lgi6iTyi+pwfXiagzqFQIAUCpVkEqNjzglxwfh6vUKVNU2AgCeWrkP86YnCo+XVdXD39vN6HN1L3kxpwxDeodALpMiKSYA2w5korSyHm88PgIRQV7CeQy8iKgrY/BF1All51filU9SAAA9u/shPNATB87cAABEhnhh+aMjROcvfHAAcguqsOzjY8KxWp0AadXXaVj+6HCjr/XbiTzh9p2jY3DvuDhhWnHcgG5QKJVmp5N48/NUxEb44g+3cV0YEXVeXPNF1MkUlNUKgRcAXMkrFwIvAMgtrEZNXROaFErR89xdZaL7W3VSROQWVhl9rUs5ZfjhUJZwf8b4eIP1XMYCrzH9ww2OKVUqXMkrx67jOVAolQaPtyeVSoW8omoozdxQQETUnhh8EXUiF7NLsWTDYdGxd58bh5kTe4qOlVfXQ6EQBx7B/h54ad5Qk9c+m1lscCy/pMaqdk4fGSPcPpdVglOXi3DykjbtxA8Hs6y6bmsKympxOP0m9pzMw9KPjmJnC5sDiIg6CoMvok5kw/fpovuL5wyCt4cLpgyPEh1fv+0sfjuRC0A9DakR183X5LXXbzuLvKJq0bE9J7VTjv9aMNbsduqu/3pn8ym8++1pFFfUCcd+OJSFK7nlZl/PXEs2HMaHP5zDtt8zAQAnLhW2+2sQEbWGwRdRJ1Je1SC63zs6AAAgkUjQs7ufcDy3sBpb92YIt3U9NDlBuP3kPX2F27X1CqzZmoaKGu1rNDWPnnm4yeHn5WpRW+8a3UN0X5MJ381VBqjUGfK/2n1Z2ATQnjTXDA/0bPdrExG1hgvuiTqJX1NzRfdH9xOvq3px7hAA6vVOj729B5rlTjK9HZC3DonEpMHd4RfghYqyGvSLDYJSpcLu4zn4/mAW3vvuDF6YPRAucpkwWjWwZ5DF7e0bGyhaL6ahUKgwfVQMdhy+hl3Hc7DreA4+XjLJ4uvrq6kzDOIighh8EZHtceSLqJP4Ytcl4fbghBA8ekei0fMkEokob5ex2ooSiQRuLuoF+J7ucnh7uOCesbEYnhiKy7nl+PM7+7D229PCjsjqOstTR0SFehs93qRQ4p6xsaJj3x/MxCMrfhPtwDRGqVIht6DKYCF9YVktnln9u8H5WTcqLWw1EVHbMfgispHv9mfgkRW/Ye23p9t8rUs5Zfhs5wXkFhoGGgDw1L19IWkhi/yovtpRMd01Xy2RSCQY0z9CuH/ychHim9eIaZKnWsLDTY6lDxsu8Pf1coVcJv7TpFmjdf6a6ZqRAPDRD+ew7ONj2Lrniuh4dr7x3ZqpzWu+8oqqsfnXy2hSKFFd14ite69g4doDqKhuMPo8IqK24LQjUQcqr6pHTX0TAn3c8eOhawDExaTzCqsQ4OMOT3fLPoorvjgBANh76jqkEokoAPto8cRWy/e46aSVCPAxnjzVGP0pyroGBTzcZBZdQ1dshOEC/7/9YRAA4M7RPfCj3rRkfaOixesdOZcPAPjvsRzMmqTNFSaXme6PlAsFWL/tLACge7AXPvn5gvDYzqPZmDmpp6mnEhFZhSNfRB3o+XUH8fcPj+KpVfsMHquoacDSjcfwzOr9wrHa+iazi1kDwJCEEFHgFeDjZlbdRN0gqqURMn36I1KF5bUI9vOw6BotmTmxJ7oFq0fiJg7qbvC4i0yK7w+qs+a35oLOKJmPp3YzwGt6yWI1gRcANCnFfb/zGFNREFH7Y/BF1EG+2n3Z5GN7T+UhTWcETKlSYfXWNPzlX/vx6D/2mP0af5nRHwsfHCDcNycoAQB3VzmeuCsJr/xpmNmvBRiOfDU0KhHs527RNfTpToEm9QgQbhsbDXx/21ls+z0T6747Y/CY/hTh21+dFG7nl6rzkbm7ytA9xBt/mtZHeEx3d+cWvenKWRz1IqIOwGlHog6y63iOycc+23lRdP8xvYDrfFYJEnsEmvU6uikkLDGyr2GW+daUG1kD5SJv22+4R+9IxP23xMHVRQZvDxfhuJuLDO88PRovvH/I4DmZNyoMjr3x+XGDYzuPZqNbsBeONxcJf2G2ekpzbHKEUIdyUK8QYbNCfYN4WvNKbjmmGK+qRERkNY58UZdW36jAvlN5+OdXJ9HYZHlJG5VKhWPn842uRWopYWm/2EDcNjTS5OP/3HzK5GP6KRN0R4hWP2t+olNr+Hoa5vI6dr6gTdeUSiUI9HUXBV4agb7mj6p5exi2bcueK1i9NU1YZxcdpt5hKZFI8NidSULRcVNSmYSViDoAR76oS1KqVFi/7SxSL2q/XC9kl6J/nGX5qtZ8cxqnM9Rld/RzUZXoZGwP8nXH8keH4y//Uq/vevTOJLi7yrD7uDg3V2tUKpXRlAn/84dBUKpURoOj9iQzsnBds0bL3roHeyHzRgXe+vNIvP7v45g4OBLRod746cg1ZN1Up5TQX7OmERvhg0wTaSeu5JajZ6R1o4tERMaYNfKVlpaGuXPnAgDOnz+POXPmYO7cuXj00UdRVKT+RbllyxbMmDEDM2fOxJ496imUkpISPPLII5gzZw4WLlyI2tpak+cS2dLpK8WiwAuAWQvVDa6Toa13qJ/yoUwn2/zofuHwcJPj3nGx6BPtDz8vV7i5yDDXwhQNdTrTYq89NkK4nRgTgL5mTlO2hbGkpNf1Sg7Zy5nm2pNyqRRrF47HjPFxGNonVAi8WvJ3vZqW942LFZLSfvXrJRbgJqJ21Wrw9eGHH+Kll15Cfb16Ie8bb7yBpUuX4vPPP8fkyZPx4YcforCwEJ9//jk2b96MjRs3YtWqVWhoaMD777+PO++8E19++SWSkpLw9ddfmzyXyBZ+T7uOXSk5uF5sGDBI27hhT3/dli7NFOTdY2LxtzmDhePGdvTpe/8/Z4S8Vbr1D7vbYcTJ1UVmcCwmzKdDX/ONx0cY3RjwTXN5JA1NaSWpFf8jpRIJpo2MFu7fNSYWPbv7YURSGDJvVOJI+k2Lr0lEZEqrwVd0dDTWrl0r3F+1ahUSE9WZsxUKBdzc3HD69GkMGjQIrq6u8PHxQXR0NC5cuIDU1FSMGzcOADB+/HgcOnTI5LlEtvDJzxfw1a+XDWogAsClFgo5r/z6FB5Z8ZvoWJPCvDViL88fhuR4y8vvKJUqKFUqHL9YiJ+PqlMefH8wy+LrtCepRIKNiyeKplj1s9G3t4ggL0SH+eDxO5NEx386cs3o+S3l9GpJQ6Ph/88HbomHi1yKb/ZmoK7B8iz+RETGtLrma8qUKcjN1a5LCQ0NBQCcOHECmzZtwhdffIHff/8dPj7aX79eXl6oqqpCVVWVcNzLywuVlZWiY7rntiYgwBNyueGv7q4mJKRjRxk6q5AQH9GCemM7EbcfyMRj9yWjurYRRWW1iNFJAJqeWQIAKKtrQq8odTqEuxZtN7jGtaIaDE0Mg0qlgkQCJPYIxND+3Vps28aXJuPR13cZHA8K9kadTjmd66V10E1DZYv3gjmvMWF4TJt3PJrj7ok+GJQUjqff1gbBmvZ99tM54VhcjDjQnTIyBv89cg1jBnRr8d/TqNB2rua8kBAfzJjYE1/vuoR9Z27ij1ONl2zSfx5Zjn1nHfab9ezZd1YtuP/pp5+wfv16fPDBBwgMDIS3tzeqq7XTONXV1fDx8RGOu7u7o7q6Gr6+vibPbU1pc56eriwkxAeFhaxFZy6VSgUVgOBgH/y477JQosaU5PggFBZWYuG7v6OiphEvzRuK3ak5mDVRm+vpl0NZKEyowjsmdiO++tERfLxkEhRKJVQqQKVUtfr/TAJgxZ9HYsUXJ/Di3CF495vTyC2sxmc/pqOyRjtCt+yDw8Lao6hQ7w5/L7T2fnt5/jDkl9agrNR2a77c9WI8Tfu2/qrNqVZQUCFK+vrg+DiMSgxttc/cdAJI3fNu6R+O/x7Owte7LmHr7st484kRCA0wXPvGz6f12HfWYb9ZzxZ911JwZ/HP1e3bt2PTpk34/PPPERUVBQBITk5Gamoq6uvrUVlZiYyMDCQkJGDw4MHYt0+d2Xv//v0YMmSIyXOJ2ts/vjyJx/6xB/f+z/f46MfzKKsyTEA6fWSMcDunQD0CW1GjTuXw+mfHcSQ9H+9+q03oufNYNt7adEI0ipbUIwALZvQXXTe/RL25xNTuOn2hAZ5Y9cxYBPt5ILdQHcz8Z/9V0W5I3UXfL80zrIloazHhPhieGGbz151zWy/R/dYWw0ulEkSH+bSahd/DzfjIururHA9MiBde619b0ixoLRGRIYtGvhQKBd544w1ERERgwYIFAIBhw4bh2Wefxdy5czFnzhyoVCo8//zzcHNzw1NPPYXFixdjy5YtCAgIwMqVK+Hp6Wn0XKL2dimnTHT/zSdG4q1NJ0RZ4LNuViA2wheZNypQWlmPjTvOQZ9uQk9vDxeM7R8hKjsz9/beCAvUjoQ89+7vqGwO4Hy9DHNXtcbX0wUVNY144u4kFJTWGh2xs8U0n6PqES7On1ZVo8179vL8YVaXOrptaJTJNXUj+4bjox/PAwDyS2utuj4RkYZZwVdkZCS2bNkCADh27JjRc2bOnImZM2eKjgUHB2Pjxo1mnUvUnvTrI7q5yhDs54GVfxkjWjh/LqsUGxdPxMXsMnz92xUcPNPyrrZ3n1NvINENvrz0koNW1jQiOT4IgxNCMKxPqMVtf+OJkSivakC3YC/U1DVh+4FMMNOBVlSot+i+bqwVE279Gg5vDxfMm9LbaN4y3TQkmkStRETW6ro/n6lT003JAACP3aFdKP3XmdpaiBFBnpBIJOgTE4C/zxuCu8f0EB578Y9DRNdYvcAwe/yIpDB46dUg/OvMAVj44ACMH9ANHm6WL6v0cncRAgBPdzk++ttEg5qKXZmba8dtvJkwqDsSovyNPzZQvXEiO78KJ5n5nojagMEXdToVNQ342/rDomODE7RlZPrFBeHd58ZhZN8wPKOzVksuk+LecXHCff2SQb5e2uzx/3xqNN55ejT+fHdfYZrr2QeSMTIpDP0szJLfGolEgpfna/NcDbAibUVnpmzeAmpNOg9LJMcHC7fXGinsTURkLpYXok4nRa/W4A8r7zHY1eLt4YIn7upr9PmLZg9EbV0TKmtNJ/8N8jOsOTiwZzAG9gw2cnbbRYZ64/9euAW/ncjDqH6WF8TuzBTNwZenFaOMlrAmeSsRkTEMvqhTUalU+GLXJeG+Zo2WJTRlelQqFb745RKq6xwjuaaLXIYpw6NbP7GL0ex27OjgqKKalTiIqH1w2pE6lf8eEydP9fawfLehhkQiwT+eHA1AnU6CHJNm2tGa2pyWqKgRB1/6mzqIiMzF4Is6lS3NNRDbi6e7HBsXT8QLswe163WpfahUKmHasaNHviYO6o6ESD/h/o1iJn4mcjZnrhbjn1+dFP5u2AuDL+pUQv09hNsvzB7YLte0Nm8UdTwVtNOBpy537A5EDzc5/vaQtij6Sx8d7dDXI6L2pWpOknz+WimOnLlh17Yw+KJOZXTzYvQXZg9EUvPaLeq8VCoVruSpC6LLzKwm0BZSiQR/vtv4Rg0icmwpF7SbsYor7JssmcEXdSr1Ter0EK4uLMLeFaReLMS3+64CABbc37+Vs9uHsSSsROTYqmob8aXOZqwmnRJx9sDgi5xGXUMTjp3PR5PC9IdGoVDP48tlnCrsCj7beVG4HR1qfXZ7S4QHGhbVJiLHtuW3K0LdXgCob2TwRWSWjTvOY8P2dOxKyTF5jq12vpF99Y1VTynX1GvTgNgqD5duXc3qusYWziQie6tvVGDl16dw4MwNROuUJmtsUrTwrI7H4IuMqqlrQnpmid2202fkleNv6w/hSl65kMcp9aJ6QfXWvRl48/NUnM4oNniekPOJwVen5ij/ey9ll9m7CUTUgs92XkB6ZgkAYP70PsLxBjuPfDHJKhm15ps0XM4tx+xJPTF5WJTNd/x98EM6isrr8ObnqZDLJPD3dhM9fiWvHKu3puHjJZNExzW7h5mNvHNzlOD6bFYJBumUriIix3I4PV+43SPcV7jdwJEvckSXc9U7yDb/dgWP/mMPACCnoApvf3kCjTZYqFhYpi2MHR3mYzInS2llPQB1MPbd/gzsPZkHAC2uCyPnV1vvGFUH9pzIc5i2EJH5GjnyRc6gsUmJlz8+BgBY+tFRrHhyVIe9lm7gNDwxFE/e0w8A8Nd1B1BWJc4yvui9g/BylxuUAHLv4Dp/ZF+aHwf2EuLvLvxA+GZfBube3tuu7SEiyzQ0cuSLnMCf39kr3C4o0+ZHqalrxKGzN6BQtt+viNf+fVy4fc/YWOH20D6hRs/XDbxmTuyJ1x8bIUq2Sp3PQ5MT0DPSD75ergCAUX3DbPr6rz82Urh97Fx+C2cSkb1k3qgw+VhhmX3zfHF4gNrkmdW/AwAyb1TiockJ7XLNnIIq4bZubcbBvUKw+3iu0ec8fW8/ZBdUYspw269PI9u7dUgkbh0SiSt55fj3zgu4Z1ycTV9fvOOR045Ejkj3h/wj0xNFj53PKrF1c0Q48kVWyS+twY3iauH+r6nGg6K2kkm1b9E+MQG4dXCkwTkTB3XH0D6hmDE+noFXF9Ozux9ee5QjnUQkpp9KYkz/cDu1xDiOfJFRvaP8cTGnzOTjyzYe67CF9wlR/rjU/Nq6IwwAMOOWOKigwm8n8oRjYQH84iUiIq23vzop3HZ3lTncD3OOfJFRbq7Gy/MMTgjB43cmwcPE4+1BE3gteWiwQfDl4SbHH2/vjWdmaEvJSJhWguwojBnviRxORp52vdd9OssSNN8psyfbd5MMgy8ySpPA9MW5Q0THH5qcgFH9wvH64yONPa1dJUT5m3xsMHMrkZ0tf2Q4AKBbEIMvIkegVKpw7Hw+/rUlTTi24s8jMXlYlHD//vHqQCyuu5/N26eL045koL5BO1euP1Lr6a5+y+guhLc3R0m4SV1LRLA66GKeLyLH8GtqLr769bLoWLDeelDNTIm9qrdocOSLDFzILhVue7mLgyw3l46bbqyua8QjK34z+/zFcwahf1yQwy2kpK5BJpXCzUWG2nr75gsiIjX9wAsw/HFe1Jyf73pRtcG5tsTgiwycvardguuqs+YqooXplWEmcnCZK7+0Bgua01aYq3d0AJ6fOQDurhzAJfvwcJNx5IvIxtKzSvDLsWyrnrvreA4A4N87zrVnkyzG4IsMnMnUFqzW3SHy8vxhJp/T1mnIjkpVQdSRPNzkqGHwRWQzSqUKKzefwubfrhj88HGmWRAGXySSX1qDglJ15t8+0f7w83bFPWNjMXVENFz1phznTdXuFtnTXFMxO78SO49mWzSffj6rxCB56gf/M8HKfwGR7Xi6yVFb3yR6vyvtvJaEqDM7c1U7OFBV2yjcVqpUOHjmpujcj/420WbtshTna0hQUdMgTDnOm9obEwZ2ByAu8aNrwsDukEkl+OSnCwCA1VvThF2SIf4eGNLbvB2J/9x8yuCYXMbfBeT4PNzkUChVQs670xnFWL01DU/f289kOSwist6ab04Lt09nFOOLXZdw/y1xSM80zFgvdeA0RAy+CABwNrMYq77Wbs/tFxto1vN0FzNqAi8A+O1ErtnBl773nh9v1fOIbM2juYC7Zupx9Vb1Z+j9bWfx8ZJJANTTJI78JUDkiJQqFQ6cvoHk+CD4e7sZPeeLXZcAAN/uuwoASI4Pwl1jeqBbkJfJzWFymRRNio5JEG4JDi8QAODU5SLhdkSQJ4L92pY1ftyACINjBWW1KCqrxXf7r+LJlXuNVpX/y339hC80Ikenea+aWnS/Zc8VPPb2HlTUNNiyWUROp6pWvdt9TfMPmOMXCvDpzxfwjy9PtvJMtYen9sZzDyQjvpsfPNzkJn/wuLk4RtjDbzkCACiU2nUq/eOC2nw9Ywvwl2w4LLqfnlmCQXrJUh0pfxhRaxTNv6DLq4wHVzuPqndknbxUiFuap/GJOiPNukdry/is+UYddKVlFOPxt/cI30n5JTUGr+HmIkO93o93cz9fri4yVNfZf5OMY4SAZHe6wVe/OPOmHFuiNGNUd+13Z7D229OiY72jA9r82kS2cvCseoHv1r0ZBo/pTm1oNqQQdVavf5aKZR8fs+q5N4qrReWAdL+PdGmO6wdeGxebv7B+4YMDAADPzRpoYSvbF0e+CIB6XYpG7xbK+ujz9XIFoB6x0t15ol9R3pSTOtOdsRG+Zr8ukSPJzq80OLZYZ6R3dF/n2QJPZKnv9l9F5g118KRSqSwe/fr7h0fNOu9w+k2jxy15vahQb3y8ZBJCQnxQWGj4ubUVjnwRAOBKXrlw20Vufhb7frGBmDulN5bNH4qwAO06sR8OZpn1/L/P09aOvGtMD7Nfl8iRKJQq5BaI/5CXVtYLtwN93W3dJCKb+fFQlnA7r50zx49L1q4frq61/3Rhe2HwRQAg5PaylEQiwcRB3RHs54G3/jxKOJ5dUNXqcxOi/BHfzQ93j+mBmHAfDIhv+1ozInvZ20Ki4J1WZuMmcjbLNh7D5/+9aPJxlUqF8qp6k4/r0+SXrG9UYMueK21un6Ng8EUd6mJ2Kb7dlwGFkUVgl3LKAAD3jovDy/OHWb1Qk8gR7DlhOviq5G5H6kJaWuP489FsPL/uII5fKAAApF4sbPFamkX2uTo/6G8fFiXk0Zt7e0Jbm2sXXPNFHUqzTdjYDspAX+O5W4icRfdgL2GapUBnV5a+wuZivkRd3TfNm1Pe33YWvaP8cbH5RzgA0f3//eNgvLXpBDQFI05d0a4Pvn1YFLzcXTAqKQzJPZ1zxoTBFwFQJ0vtyLIoxn4JdQvy6rDXI7IFD3fz/oT6ejKFCjm3uoYmrN56Gn+a1gdhgZ5WXeN0RpHo/sWcMnQP8cKT9/RDXX0TokK98eTKfQC0aYdUKhWKymqx4/A14XmaNZT6qYqcCYMvAgDMurUnvtp9GY/ekdhu19T9pXL0XL7B46P7cQcYObewAA9cyVVvVgn0dUNFdQOaFIY/Yu4aY7xEF5GzeHrVfgDA/35wRKjeAMCiOr6rt542OLZ03lBR3eC3nhgJV508XicvF+HY+QJrm+2wuObLCqWV9UbXMDkzWXM2YBd5294Si3Ryp7yrU4PLtTmrcGSIdrSL9RvJ2d02JEq4PX5QJGQm3tOWfEEROZKK6gYUlYs3ZB07r/4x3dikwPFW1my1xlWvDFBYoCcCfNyEeqnl1Q1odIByQO2NI18WKiqrxd82HIaXuxxrF7IGob7EmAAsmNEfZVX1qGtUYOse9fy+i0yKhkYlVACevT8Zu1NzMMBJ5+qJNOQ6P1YmDY3CzsNZRs/TTwpJ5CwWrj1gcGzD9nQcv1DQ5sCrJTV12ryRf587BK98kgIAGG+kdJ0zYvBlIU1Ga0vKE9Q1NOGnI9mYPDQSPp6uHdW0NmmvH+ZSqUQ0D68JvjT9lVdYjYG9gjGwV3D7vCCRHenWJ43t5oe6BuNBlqnjRM5KP/DST7RdW98kqtNr6eiv7ndldJgPQv09UFBWix6dJBk3530sdDm3zOCYSqXCt/syRIlKdT29aj9+PJSF5941/AWhLz2zBMfO56OozLq8W23FdA9E5qvW+XUOAK4mpu3rGXxRJ7P62bGi+yP7honub9xxXnT/6vUK6PvLff1NXl8z7aixaPZA3DsuFmP7d46RLwZfen44mIlTl4tMPh7fzU+4/XvadVTXNSLzRiV2HL6GNz9PbdNr55fUYOXXp7Bhezr+pleEui2UShW27r2CazdNl1IQiqK226uqbVw8EUseGoykHqzZSJ2PXCr+EzqsOfeQvjpOO5ITutlC+hRfvVmcWwZ0E90/cUk8Mqa/6WrR7IEY0tv0bkX9cYAQfw/cPSa206wV5rRjM6VShZKKOvzn90wAEO3m0BWqU0Lnk58v4LP/XhS9GRRKJWRSKW4UVyPQx92iBezf7jMszttWZ68W49j5Ahw4cwM/H8nGy/OHISbcp91fxxSJRIKEKH88ekcSFr13EHeMirHZaxN1tIQof0wY2A0jktS/+iVS4z9fOPJFzsiS5MDdQ7wNjmm+DwEgPatEOD5zYk/07RHY4vWiQr1x1+geSO6klU8YfDVb/u8UZOe3XBKnoLRGNJR6/y1xSLlQIHre42/vxZtPjBQKhUaFit+Q125Wmgx+zmSWiO6rpzOvYlS/cHQPtjwn1iMrfjM4tvLrU/jj7Qk4e7UEf5reR5hm7Oi9WAE+bvjwbxOEDyJRZyCVSjBvah/t/ebYy9/bFWVV2i8uLrgnZ1RR3dj6SToG9QrGSZ2Zo+0HMjFjfDwA4EaxdhQt2K/1WqcSiQT3jY+z6PWdCb8Jm7UWeAHAobPaiup/nzsEd4zqgVf+NBxD9YZOf9Op8ZajV+Pw1U9TTLx+pcGv4//7Ph0/HbmGpR8dNXuxYpNCiSPnbhpNmOrr5Yqq2kZs2J6OA2du4Kcj2qR1muirI5d8MfCizk74MaPz8ZOAC+7JOV0vEn9/6U/53TokEgAwbUQ0AOCxO5NEj+84dA3ns8SDCgDQK9LP4FhXY/a3YVpaGubOnSvc37VrFxYtWiTcP3XqFB588EHMnj0b69atAwAolUosW7YMs2bNwty5c3Ht2jWT5zqyzb9expY9V9AjXL3L4q7RPRDfXfvm6adXOkd34f3Sh4dibLJ4gWB5teFQrmYbrS7dxHK6gV9LnvjnXnzw/Tk89o89Bo9NHNQdMyf2FO67yGUG57T/qi+irkMbfKnw97lDsPDBAXB1lXHakZySpnSWxgMT4uEql+Lhqb0BAA9OiMeCGf2FESoPNzkWzR4onC+VSvDBj+dEG8iWPjwUft4sLWdW8PXhhx/ipZdeQn29uhL566+/jpUrV0Kpk2j05ZdfxsqVK/HVV18hLS0N586dw+7du9HQ0ICvv/4aixYtwooVK0ye68h+ScnBzqPZUDUPD+lunwWAUX3Fmdp1FxHGRvgiMUa82Px5I3lT5LKWg55Nuy7hht4HwVKnrhRhavMvFAD4z/6rwm2mgCRqu+gw9TKDPjEBiO/uh+T4ILi7yLjgnpxS6sVCSCTA3WN6AACS44Ow4YUJuGVgdwDqBKmDEkJEI2LhAdrSQ/eOi0V5VYNoxie2k6SKaCuzgq/o6GisXbtWuD948GC88sorwv2qqio0NDQgOjoaEokEY8eOxaFDh5Camopx48YBAAYOHIizZ8+aPNfRGFujoYk1pXqLavUX1adlFAPQbjvXLMZtibGSJKL2NCiw8otUNFmQ6fftJ0eJ7uvvdqxvVGi3ymt2O3Lgi8hq45O74dkHkjF/mnYdmJurDPUN5ucFJHIEBaU1UChVUKmAe8fF4YP/mYBwM2o6Bvq6YcrwKDw/cwCmjVRvsLIkL2ZXYVbwNWXKFMjl2tGe6dOni/JBVVVVwdtbu7Dcy8sLlZWVBsdlMpnJcx3NnhOGhaA/+CEdgHZRrSmaWm+3DY1qPl8izI2bYio/kMbIvmG4mF2KHw5mGTx2+OxNlFbWi46F+nsg2N/D4Fx972w+JbrP2IvIelKpBAN7BsPdVfv30s1FhrKqBoOcYESO7Ny1UtF9c1M8SCQSzJrUC/3jgiCVSDBhYLfWn9QFtctuR29vb1RXa6fEqqur4evri7q6OtFxpVJp8tzWBAR4Qm50jVL7GNE3HEfTteuqfj6ajftvS4Cnu4twTJP0zdfXAyEh4h2LPp6uBttyA/215z12XzJ+1VmIHxTkLRpBa9BJKNcnJgAX9N74C/8wBM+u2osfDmXBzd0FM29LgIebHFt/vYTPflLvwPxh5T2IDPVGbkEV5k5PREiID7oFe+G6znRlSIgPxg3sjt9PqYPLazcrERLiA6/mOXhfP8N/W2fQGf9NtsB+s4yx/tJsunl/WzreXjDO1k1yGnyvWaej+u2znRfb5TX2nrou3O4XH+RQ/5/t2ZZ2C75cXFyQnZ2NqKgoHDhwAM888wxu3ryJPXv2YPr06Th16hQSEhJMntua0lLTyd7ag4+HuCsqaxrw1c7zmD7SMC9VTXU9CgvFo3USnVVTcpkETQoVDp2+jkk6Uf+6hePwzOrfAQA5eaWiwE7XC7MHGiyYr6mqw6I5g7F43QF889tlXMgsRrdgL/ySkiOcs2zDQfh4qK8Z7O2KwsJKvPKnYVjyf4dRUlGPObf1QmFhJebd3ksIvgCgsLASlc0jZxUVtQb/NmcXEuLT6f5NtsB+s0xr/XU+q4T9aQLfa9axVb+112u4yaUO8//ZFn3XUnDXbnm+Xn31VbzwwgtQKBQYO3YsBgwYgP79++PgwYOYPXs2VCoV3nzzTZPn2ltdvXiNl7eHC/57LBuuRkbb9Nd8AUBFjXZKoX9cEE5eLhLVfQMgCrYKy+oQE248+JKaWHiVFKvdVXk2swRn9fKC6eZX0SR7lMukeOOxkbiYU4p+zc9vKeWDhBOPRERd0mv/Po7MGxXw926/GsTx3XyR0VxaKLUDC3E7G7ODr8jISGzZskW4P2LECIwYMUK4P3DgQNHjACCVSrF8+XKDaxk71958PMWB0PSRMdiy5wq27LlicG7alSKMH2B8HvvPd/dFQ6MCJy8XtbjQfuexbPz57r4Wt7NfXCDOXjXMmwIAj9+VhA9/UO8cVVeEV6/5cnOVITleXMh6YM9gnLpipIwSYy8ioi4p84a2/qJEot6H5etpfJDAXBlGajoSM9wLEnsE4Oej2cL9iYO7Gw28APEIk77hierabgnR/ghtYcH70XP5eGR6H7jIZUaT0C15aDBWfHHC4HhUqLfR4OvROxIxqm84fL1cceJiIaLDWp7LvmdsrBB8fX8gEy4uTIBKRETAqmfURbOr6xrh6da2MMHbwwVVteqZIWsGHDorfuM26xcbhL/c1w8AMGlwd7i5WLa4/4P/mYAP/mcCJBIJJBIJwgI8RTtCNXTTP7z/n7PYeTQb/9qaZnBeQpS/cO7zM7XTspr8KvrGNFd679sjEHOn9DY5dakh19ldue1ApjbDfYvPIqK2KK+qb/0kog7W2KTE9gOZKCqvbfE8L3cXo99jltBkvY8M8TIr7VJXwZEvHUN6h2L1s2OFReu6vNzlQq6SJ+8xjN7N3Yarm/4hLaMYaRnFcHeVoUlhmFcs2N/DoMB3W4eANeR669aUzPNF1OEu55ZjaJ9QezeDurj9adex/UAmjpzLx1tPjOzQ1+ofF4hFswYyuaoejnzp8fV0NRrpP35XXwT7uePBifEY1s5/PBfPGSzcbm3ESmZksb+xYLA1+te5nFtu4kwiagvdIsKXcsrs1xDqkpQqFTb9chEXs7Xpi/JLakT/7UgSiQR9YwPh6c6xHl0MvsyUHB+Et58ajWkjYto8DKsvJly7PmvayOgWzjTcqTg2OUJUzshc+hn8Tzdn5efEI1H7mtycbBkALuWW2a8h1OXkFFRh6UdH8duJPPzjy5MAgNr6JuzWyTmZX1qDXcdzTF2COghD0RaM6huOw+k30TvK32av2dr0pX6ai0emJ1r1Om6uxte0cdqRqH3dNjQSA3sF48MfzyEjrxy19U0G9WGJOsLLHx8T3b+UU4bvdGr6AsD//t8RAEBdPUsA2RJHvlow69aeGJccgUfvsC7AsYY5wU97tCfYzwPzp/XBq48MN3u9GhFZTiKRIMTfAwmR/lCpgIw8TvFTx7p2s9Kg5BwArPjihMmp7//8ntnBrSJd/PnVAl9PV/zJypEla5kz8DSmfwR2p+bCz6ttifA0ucp0i3Vz4IuoYyRE+eGnI8Cl3HL0iwtq/QlEViivqsern6aYfHxgz2BMGtwdq7YY7rIn22Hw5WDMXU/28vxhHdSAjrksUVfXs7sfJAAuc9E9daCrrSQ1ferevrh2s8pGrSFTON/kIKYMVy/K7RsbaNd2yLjoi6hDeLq7oHuIN67eqEBjk7L1JxBZIbew5cDKRS4TbfLSt/Thoe3dJDKCwZeDmDmxJ9YuHGf3XCjFFUwCSdRREqL80NikxLV8xyguTJ1PgxmBvYtcirULxxl9LCrUu72bREYw+LKDObf1MjgmkUjg5d4+CVTbIjzQ095NIOq0Epp3TnPqkTqKsSThxhjLGQmYnzCc2oa9bAe3Dom0dxOMum1IJHpG+tm7GUSdVq9IfwBMtkodx1gaEw83Gf54ewL+9odBwjH9nJFkW1xwbwcSiUQoNtrWHYvt4cW5Q7ArJQcPTIi3d1OIOrUAHzcA6tJiSpWq1YoWRJb65OcLBsfm3t4bI/uGi46ZGvki22DwZSdjkyOw82g2Rva1f6HRnt390LM7R7yIbKmorBahAZzmp/aTnlUi3I7v5ouwQE/cOy4WwX4eBufqJ+wm22LwZSf3jYtFXIQvBvRkvh+irmREUhiOnsvnqBe1K4VSic2/XoYEwLL5w1rc0Uj2x+DLTlzkMgxt5wLdROT4XJoXNCtVKju3hDqL0xnFOJNRjLzCaoxNjrA68NItAk8di8EXEZENadY5M/ai9nDtZiVWb1Vnq3dzlWHG+DirrzVKb10YdRxudyAisqGaegUAoEnJ6Iva5kxGkaiU0J2jYuDv7WbxdRJjAgAAST0C2q1t1DKOfBER2dDxCwUAgINnbmDmxJ52bg05s+UfHRHdv31YlFXXeWH2QFRUN8DPisCNrMPgi4jIDgpLa+3dBHJQ6ZklOJJ+E3+antjirkQ3VxnqGhTCfRe5zKLXWTR7IBQKFSQSCQMvG2PwRURkB2GBnlAo1aVgmPCSdK38+hQAYEjvUAzsFWzyvNAAT5RXNQAAZk2yfBS1bw/71hLuyviJJyKyobAAdc6lkoo6PP72Xjz+9l77Nogc1q8nclt8PLdAW0R7yvDojm4OtSMGX0RENpTfPN145Fy+nVtCjiileU0gYLxUkK7a+qaObg51EAZfREREDuLU5SLhtoerZWu4yHkw+CIisjMVk35RM93ku2lXilo4k5wZgy8iIjtrUijt3QRyEEqd/G/B/oY1GTUyb1QIt1f+ZUyHtonaH4MvIiIbighSF9OOjdCWgGlsYvBFarprviYPNZ2367V/HxduB/gwTYSzYfBFRGQHmhqPAIMvMk4uY/H1zorBFxGRDWmW9MjlDL5IbOOP50T3zalAFRpgemqSHBeDLyIiG7pZUgMAKK9uEI41cs1Xl6dSqXDw7E3RsfXbzmLZxmMoKBNXQyirqkf3YC8AwCyWqHJKDL6IiOwgr7BauM2RLzpztdjo8dzCKizZcFi0C/Kv6w4ir0j9/gnyc7dJ+6h9MfgiIrKTgT3VpWMYfDm3X1NzUVTetlqdN0tafv43ezMAGKYlyS2sMnY6OTgGX0REdhId5g2AwZcz++1ELr7YdQl/W38YGXnlVl9H0sra+p1Hs7E/7Tqu5VeKjocFeFr9mmQ/DL6IiGwo0FedFmBo7xC4NC+655ov57Xpl0vC7Tc+T+2wnG1e7nJ8/t+LWP7pcdFxTjs6JwZfREQ2dMfIGADA6H4RQroJjnw5J91EpxoKc7YoNmtsUuJidil+ScnBd/uuCsfnTe1tcO4zM/obvbaLnF/jzqjlqp1ERNSuJg6OxKh+4XB3lWPPiVwADL6c1bbfMw2OWVIq6s/v7DV6fMLA7vhs50XRsd7RAejbIwDpWaWi465y1n90Rgy+iIhszN1V/afXpfmLk8GX87mSV44zV4vRO8ofF3PKhONKK/5XPjI9EYkxASiuqEN4kOk1XLHd/ETB19JHRnDky0nx/xoRkZ1wzZfz2va7eprwvvFxouNKK4qkj02OQJCfOxKi/OHr6Wrw+EeLJwIA/L3Fj/WK9rf4tcgxMPgiIrITIfjiyJfDq6ptxJqtabh2sxLHzufjXFYp+sYGIiHKX3SeJdOO5pI2b4UsKK01epycD6cdiYjsRBt8KezcEmrNLynZSMsoxqXcctTWNwEApgw3LHxtwXr7Fr08fxhe/TQFT93bTzimO0I6fWQM/LzdUFjbYOzp5OA48kVEZCfc7eg8NANaDY3aQLl7sLfBec+vPWB0F6SlYsJ98PGSSRjWJ1Q4Nlzn9gMT4tv8GmQ/DL6IiOyEa76cR32DOujSTfdgaorxtX8fN3pclzXTkwlR/nhwQjxee2yExc8lx8Lgi4jITjTBV1FZHf751Ulcu1nZyjPIXnan5hoca8vyrpwCy8sCSSQSTBsZIxTVJufF4IuIyE40wVfKhQKcv1aKd789becWkSX8mncfzp7U0+Cx8uqW12K98klKh7SJnAODLyIiO9Gs+dJQttdqbbIJefP/v9uHR2Ns/wjRY6//23RwVVJR16HtIsfH4IuIyE6YILPzkOr9ryyuqDd5roQpIro8sz75aWlpmDt3LgDg2rVr+MMf/oA5c+bg5ZdfhrI5ne+6devwwAMPYPbs2Th9+rTF5xIRdTUMvpyDOYlTT10uMuuc9KwS0bH7xsVa3S5yXq1+8j/88EO89NJLqK9XR/FvvfUWFi5ciC+//BIqlQq//vor0tPTcezYMWzduhWrVq3Cq6++avG5RERdDYMv51BSbjhNOEtvnVdFTWOr13n329NYufmUsNNxZFIY7hrD4KsravWTHx0djbVr1wr309PTMXz4cADA+PHjcejQIaSmpmLs2LGQSCTo1q0bFAoFSkpKLDqXiKirkcsYfDmDGyU1AAC5TDtdOGV4tEXXUOgUfdQMpHH6setqNcP9lClTkJur3WKrUqmEN4yXlxcqKytRVVUFf39/4RzNcUvODQwMbLEdAQGekLN6O0JCfOzdBKfEfrMO+80y1vSXi1wqJFktr27osn3uyP/uyvMFAIA7x8Zh274MAIbtnTc9EZ/9dB7z70jCpzvOAQBultejf89gAEBZpXYN2LaDWQAAlaTt/25H7jdHZ8++s7i8kFRnVWF1dTV8fX3h7e2N6upq0XEfHx+Lzm1NaWmNpU3tdEJCfFBYyDxAlmK/WYf9Zhlr+0suk4oy3HfFPnf091r6lUIAgKtUO1Kl394JyRGYkByBlAsFwrEX1x/EhkW3wNVFhrwi7ffewdPXAQBHzt5s07/b0fvNkdmi71oK7iwe805KSsLRo0cBAPv378fQoUMxePBgHDhwAEqlEtevX4dSqURgYKBF5xIRdUVc9+X4DqfnAwC27r3S6rkx4eIv3CaFeo6xqoY1GEnL4pGvxYsXY+nSpVi1ahXi4uIwZcoUyGQyDB06FLNmzYJSqcSyZcssPpeIqCvSz/VFjisqxBvZrWSm93YXf63uPp6Du8b0QKWRBfm3Dols1/aR8zAr+IqMjMSWLVsAALGxsdi0aZPBOQsWLMCCBQtExyw5l4ioK+LIl2OrrW8SbveJCUCfmAAMbF7HZYz+IvptBzJRUlmPqFDDItwyKRfcd1UWj3wREVH70Q++auub4OHGP82O4i//2i/cDvJ1x+RhUS2eL9ULqGLCfLA/7brRc39JycHsW3u1vZHkdPiTi4jIjvSDr65Uembzr5fx3f6r9m6G2Ub0DWv1HN10FBIJsPihQR3ZJHJSDL6IiOxIf81XSaXpsjSdzS8pOfjxUJaQdNTRyfVrCBkh0zlHKpHA3VWOaSMtywlGnR+DLyIiO9KfpupKI18aN4udJZWQeUFi92AvAOqRLwBwZY5K0sPgi4jIjvSTnLdUkLmzuphdau8mGKU/IiczY+QLADyEHY/q/7muJjZVDOpleuE+dW4MvoiI7Eh/v1tpFxz5uuSwwZf4vpureSNYXs0bJpoU6uS5ciPB18ikMPxpemLbGkhOi8EXEZEdpWeJA4/iLhJ86dY6vHTNMYMvhdK6tWieerm+9DdV3Do4Ek/c3RfeHi5Wt42cG4MvIiIH0lUW3Dc0aoOvjLxyUYklR6EbIFrC010cVOlPO04e3nK6Cur8GHwRETmQkop6p9n91xa6yUubFErktJI53h6UOiNf/3pmjNnP83TTH/kST1fyi5f4HiAiciBNCqXRUjSdjW7wBQAZ18vt1BLTNNOOQ3qHwM/bzezneelNOyr1pi/1d7hS18Pgi4jIwZRUdv51X/WN4im9zOsVdmqJaZrgy9IyQB56wZd+sKVfgoi6HgZfREQOpri886/70owOubvK4O3hgqsOGHw1NK9DszT48nQTr/mKCPQU3dcfGaOuh8EXEZGD6QojX+XVDQCASYMjkRAdgIKyWlTWNNi5VWKXsssAAFGhPhY9z8NNvMYryM9duL124Ti4ujDpalfH8JuIyMF0hSz3Fc3Bl6+XK3x93HHiYgEyb1QgOd5xEo+ezigCACTHB1n0PHdX8Verh5scL80bikBfN3i5M70EceSLiMjhlHSBLPcVNZrgywW9YwIAwKGmHpsUSqRnlSDYzx0RQZ6tP0GHfp4vAIjr5gt/CxbtU+fG4IuIyIHIpJIuMe2oGfny83JDryh/AI4VfF3OLUdtvQID4oMtXiDPNV3UGr5DiIgciL+3W5cY+covrQWgnnb083ZDqL8HMm9UQKlSQeoAuwGFKceelk05AoCPpyvuHtMDPSJ827tZ1Elw5IuIyIEE+bqhrKpeqAvYGdU3KnD0XD4AwM/LFYB6Wq66rgn5JTX2bJrgdEYxXOVS9In2t+r5946Lw8CejrN+jRwLgy8iIgcS6OsOlQooq+q8o1+6uxo166Niu6lHiRxh6rGwrBY3imuQGBNgkJ2eqD0w+CIiciABvupF2V1h6hGAMMUY380PAHD1hv2Dr9MZxQCAZI5cUQdh8EVE5ECCfNU5obrContdUaHekMskDjHylaZZ7xVn+XovInMw+CIichASAIE+6uCruLzzBl/GFtS7yKWIDvNBbkEVGhoVZl2nsUmBc1klBrUT2yK/tAZnr5YgMsRLlByVqD0x+CIiciCBzdOO3+676nAZ39uLJru9vrgIXyiUKmTnV5l1ndc/S8U7m09h24Gr7dKuA6dv4H//7wgAoFuwV7tck8gYBl9ERA4k0Fc72pJ5o9KOLek4ZzNLjB6Pa150n3KhwKzr5BSog7QfD11rl3Z9/NN54XZCc+4xoo7A4IuIyFFIxAk6XeSd80+0fqFpDU3wtet4ji2bAwCobxBPdXbnyBd1ICZZJSJyILrZ1OUy+ycb7QgXskuNHg/x9zDr+d8fzERYgGUlf1pz/pq4TV4erMFIHYfBFxGRg5BAHGydzypFr0h/+zSmg1TVNuK3E3kAgFi9DPASiQRurjLIpRKczig2WdB62++Z7d4u/XqMPp6u7f4aRBqdc0ybiMhJhAVoR3v0NwHKOuHI15Y9V4TbwUZ2E0olQHVdE1ZvTUNhWa3N2qW/Y1KTeZ+oIzD4IiKyo1mTepl8zBFqHLYnlUqFA6dvCPfnTultcI7u6N9/9rfPLkZz6I98EXUkBl9ERHakO7o1bkA30WNVtY22bk6H+vM7+0T3vY2sq6qpbxJuHzmXj+tF1R3eLgBQqtovVxhRaxh8ERHZkVSqDb70CzH/fDTb1s0xW5NCiUs5ZcJ0XZNC2WoxcGuKhb/00VGs2JSKv60/ZFU7zaXQmXacMT6uQ1+LiOOsRER2JGtlavHY+XwMTwyzUWvM9+EP54R8XPOn9cGnP18AAGxYdAtcXWRobFKivlFhdHTLUpdyywGopy1vltQIxyUAVABcXaRQqVSinaKWKK2sxxufpQIA7hzdA3eO7tHGFhO1jCNfRER21Nqi+h8PZdmmIRbSTYSqCbwAID1LnUD1qZX78Oya39u19E9eUTU2/XJJuK+5ckOjEtV1TcafZIafj2qTtA7oyXqO1PEYfBER2ZHutKMxZVWOXWJoUK9gPDQ5Qbhf31yXUbOGSmEi+LptSKTFr7V1T4ZBPi6Nkgrra2He0FlXFt/Nz+rrEJmLwRcRkR3Jpdo/w8ZmzRxp0X2TQokvfrmE7Hxt2aNAX3fcqhNI5RWKF8g3NBkvkq0ZIbPEmavFcJFLjWb+f/vLkxZfT6NJwcX2ZFsMvoiI7Ki1kS9HcujsTfx6IhevfJIiHNNfRN/YJL7/3ndnoFSqkKe3a3HaiBir2nDHqBijdRd1d0laqklp+UYAorZg8EVEZEcyI8FXdKi3HVrSuiPpNw2OFetN9+mP1F3ILkNaRhG2Nefs6hHug3HJERjVz/gmgtYW6E8bEW0Q4LVVUxNHvsi2GHwREdmRbvCluTW6X7h9GtOK5Phgg2Nnr4qnD3tH++PgmRuiY98fyELqpULEd/PF0oeH4k/TEyGTGv/6efOJkS22wUUuw6WcMssa3gqOfJGtMfgiIrIjo9OODprZ3sfT9KhUfDd1ncbauiZs3HFe9Ni15jViM26JbzUdhLeHi9HRQABYNHugBa01n4JrvsjGGHwREdmRsUDDMUOvlmPCu8b0AABcLzaekT6pRwASYwLMep0x/SMMjr3x+Aj07REIABir87jubWs3J2h2aBLZCoMvIiI7EgVfjhp1NWspl5aXu3pUrLDMeMqHGePjzX6dWZN6GhyLCPISbk8bGS3cHpSgnQp9ds3vKC43/vrHLxQg80aFwfEmhRKllfVmt42oPTD4IiKyI5nMyJ9hBw3Cvtp92eRjXs0L5U0FP3HN05Lm8HCTo2d3bb6tP03rI3o8LMBTuK0/jZlTUGX0mu9vO4vX/n3c4PiLHxwRbjvqWjvqfBh8ERHZkdTIXF7alSKbt6OipgGPv70Hv6bmWvQ8Tzd1lTpPd/V/C8tqDc5pS1CT1CPAoOC47jo5/cLbZVWGo1jlRo5pjhfpBIvx3ZlglWyDwRcRkR3plheSNA95ncsynsW9I529WgyFUoUvdl3Ct/syoFJpF6ErlKan5jR1EL2agy9jS9e7BXsZOdqyOZN7IdjPHTMnGk5B6jqdUSy6/9l/L+K7/Rmo0Zki1Z0K1QSXNXVN+NeWNNFz25Iln8gSDL6IiOzI1M4+W5PozHXuOHwN+aXaEaxXPzmORe8dNPq8cQPUC95lUincXWWix1Y8OQrTRkRbVUqoR7gv3n5qNKLDfFo8TyoB5t6eIDr246FreGb1fqzacgoKpRJvbkoVHvti1yU0Nimw7rvTyNabotxx+BqIbIHBFxGRHZmT4b62DdnbrbXvVJ6QzDS30Pg6qlcfGS4stAcgug0Aof4eeHBiT7i6yPSf2m4uZJdh4mBtcPfmEyMxdbh6Qf7ZqyX44PtzBs/54PtzuJBdhiEJIR3WLqKWMPgiIrIj0Zqv5ptP3JUkOuf1zwwXirc7vRjwv8dy8Nd1B/DZzgsmnyKXiZ/k5SHviJa1KDk+SHQ/0McNU3V2Q17INpzCTb1UiD7R/nji7iSDx4hswargq6GhAYsWLcLMmTPxyCOPICsrC6dOncKDDz6I2bNnY926dQAApVKJZcuWYdasWZg7dy6uXVMP6Ro7l4iI1Eb2DRdN4d0orjHreSqVCvUN1uWs0l+43i8uEHK5FHtPXReORYeJyx656Y1oZecbHyHrSL316jy6yKWiYuWVNYa5vyQAnpmRDBe5uP0RQZ4G5xJ1BKt+pmzZsgWenp7YsmULrl69itdeew1FRUVYu3YtoqKi8MQTT+DcuXPIzc1FQ0MDvv76a5w6dQorVqzA+vXr8fLLLxucm5TEXyBERBpymRSAZYHUpz9fwO+nb+Dtp0Yh2M/DoueWVzWI7s+a1AsRgZ44d60Eq75WL0yvaSHPl61NGxGNn49m4/bhUQCA954fj9r6JkgkEoMROX3/+8chwu5MXb0i/TuiqUQGrBr5unLlCsaPHw8AiIuLw5kzZ9DQ0IDo6GhIJBKMHTsWhw4dQmpqKsaNGwcAGDhwIM6ePYuqqiqj5xIRUdv8flpdUzHzRmWbryWTSiCVStAvVjut5+/jJjpHZceqPA9O7ImPl0wSakR6uMkR6OsOQBO4mhbo62b0uIdbx61NI9Jl1chXYmIi9uzZg9tuuw1paWmorKxEVFSU8LiXlxdycnJQVVUFb2/tMLVMJjM4pjm3NQEBnpDL+cEICWl55w8Zx36zDvvNMm3trwA/T+Eawf4eonI5llzb29vN4ra46y2WDwn2RkiQOEXEtNGxWLvllHA/IS64xQ0DlrShPd9rqlaiwpAQHyFQ0xUa5O1073lna68jsWffWRV83X///cjIyMCcOXMwePBg9OnTB7W12m3J1dXV8PX1RV1dHaqrtesIlEolvL29Rcc057amtNS8NQ+dWUiIDwoL2/6Ltqthv1mH/WaZ9uivsvIa4Rp/nJwgLLR3lUstuvblrBIkRlqWMFSuF0M11jWgsFApOlZfK56aLC5ueY2XuW229Xstv6ACinp1YLvkocHYuOMc4rv7YXRiqFO95/kZtZ4t+q6l4M6qacczZ85g1KhR+OqrrzB16lT06NEDLi4uyM7OhkqlwoEDBzB06FAMHjwY+/fvB6BeZJ+QkABvb2+j5xIRkZa/t6twOzTAsoXgJVbUKtQv/+Puavjb3J7TjO1J99+WEOWPfzw5Gk/c1RdurpxdIduwauQrJiYGa9aswYYNG+Dj44M33ngDN27cwAsvvACFQoGxY8diwIAB6N+/Pw4ePIjZs2dDpVLhzTffBAC8+uqrBucSEXV1pibwQvwNp8ha4urSMVmEzMlJptE/Lqj1k+zE28Ol9ZOIOpBVwVdgYCA+/fRT0bGwsDBs2bJFdEwqlWL58uUGzx84cKDBuUREpKVbMNrSLPgurSw4t9aAnuYHVAsfTO6QNlhrTP9wzJ/Wp9OM3pFzY5JVIiIHVN+oTTNhacBg6fl1DU14f9tZk497NBfPbm0X4f/+cTAA4O9zh4iCR0fwyPREyKTSVv8NRLZg+3TERERknE7AointAxgvVt0ShdKyZ2z7PbPFx1cvGIsmhbLFcwB1nqyPl0yy6LVtoU+0v8MFg9S18ScAEZED0s051VrqBH1KC4OvHL0C03eN7iG67yKXCqNfziipR6C9m0AkwuCLiMgBBft54OGpvQGopxHLq+qxfttZ5Je0nnZHYWGw5q63yy/ARBJSmVSCYD/LFv87gluHRLZ+EpENOe9PGSKiTkZ/Ymx4Yhj+vfMiAOCbvRlIuVCAovJaLH14mMFzdacFlcrWpwh16U9TBvoYD74kEgnefmo0jqTfRLC/ZeWL7OXtp0Y59agddU58RxIROQhT04tNCiXqmgtmNzQaD6zqdApq19RbVhNSfz1XgE/Lo1sj+4ZbdH17CjARSBLZE4MvIiIHYWq68GxmCQb2DAYA1DYYL25dV689fvxCgUWv26QQv25nCFg+WjwRTU1KofYjkSPhu5KIyEHoL5TXHQk7daUIAFBSYTx7ve7Il6UUetOUXu7O/7tcKpHA1YUZ68kxMfgiInIQ+muvLNm0+OuJXKtft6lJ/EJMy0DUsRh8ERE5CP2RL1e5+X+i9526bvXrNlm4QJ+I2obBFxGRg9Af+WqPaTOlSoXvD2TiRnG1yXP013wRUcdi8EVE5CAsTY5qjrTLRdh2IBOvfJJi8pymJo58EdkSgy8iIgdhaVkgc1TVNQIQlyvSV1PfKNzuH2d+8Wwisg6DLyIiB9ERI1/mFIas1ckL9vzMAe3fBiISYfBFROQgLC0L1Jqi8lqczSxp12sSUds5fzIXIqJOQtGOC98LSmuw5P+OtNv1iKj9cOSLiMhBmDvt2NDYekLVc1mlZr/uQ5MTAAAzJ/Y0+zlEZD0GX0REDsLcBfdVtY2tnpOeZf50o0tzPjFfLxezn0NE1mPwRUTkIEwV1tZnTvB14ZrhyJep62tG3KTMbE9kEwy+iIicjDnBV3WdYQHunIIqo+dmXC8HABSW1batYURkFgZfRESOwsyBJ3OCL2POXC02evx6UQ0AoLTSeNFuImpfDL6IiJxMdSvB1/xpfYzGcZU1xp83IikMANA3lglWiWyBwRcRkZNpaeRryvAojB/QDdFhPgaPmVpSJqz54jcCkU3wo0ZE5GSqag3Xc2mEBXoCAJJiAwweU5lId685LuGCeyKbYPBFROQgJGYu+mpp5EuTqLVvj0CDx1od+WLwRWQTDL6IiJxMdZ3p4KtJoS6g3SvSz+AxU6kmNEW3Oe1IZBv8qBER2dltQyMBAIkxhlOFxuiPfH2x65Jw28/LFQDgIpcZPM/UyNf3B7MAALkF1Wa9PhG1DWs7EhHZ2R9u7YW7x8TC28O8DPP6wdevqbnCbd/m4MuY1pK41tRbl8KCiCzDkS8iIjuTSCRmB14AUGUiZQTQcqowY9WLyqq0ub1cjYyWEVH7Y/BFROTA3FwNA6Ka+iYolEqj57e8Y9Ew+jqnUwNybHKExe0jIssx+CIicmCDewUbPW6sfBAAuLiY/rNubOTrbKY6+Fr+yHD4e7tZ3kAishiDLyIiB2Zs4TxgOst9XISvyWuVVzWI7itVKpzLLIGflyu6h3hZ30gisgiDLyIiB+YqN/5n2lSur5amHfVrO+YWVKGiphF9YwOZYJXIhhh8ERE5MFPTiMaCryBfd4uund485dg31jAhKxF1HAZfREQOzNQORGPB1z+fHi2679I8ahbq72H0Gpr1Xsay4RNRx2HwRUTkwFz0ph3dm3c/VrdQ31FDM2UZ1027DkyzS7K+UYHLuWWIDvNuMTcYEbU/Bl9ERA5MP/jy9VQHSi3Vd9SYPCwKADCyb7hw7Eh6PgDgYnYZmhQqTjkS2QEz3BMROTD9Bfc+ni4oKKtFVW2DiWdo3T0mFpMGR4oSuG7ccR4bd5zH0D6hAIB+nHIksjmOfBEROTD9NV8+wshX69OOAExmzj9+oQCucil6Rvq3qX1EZDkGX0REDkx/2tHLQw4JgBOXCvHiB0egbKVeY0t6RwcYXJ+IOh4/dUREDsxYcOTprl4xcrOkBvUNCrOu0zvKH2P6h4uOcb0XkX1wzRcRkQPTX/PVs7sfLueWC+WFzB34WvzQYADAwTM3hWP9GHwR2QVHvoiIHJiLi3bNV0KkH8b0j9Bbx2X9tGNEkGcbWkZE1mLwRUTkwHRHvh69MwlymVQUfFkferVcioiIOg6DLyIiB6a75kvaHCyJgq+2RF9EZBcMvoiIHJhu8OXnrU4zYSp9hCVcTdSMJKKOx08fEZED083zJZep/2R7iUa+LBv66hPtDwCIDvVpe+OIyCpW7XZsbGzEkiVLkJeXB6lUitdeew1yuRxLliyBRCJBr1698PLLL0MqlWLdunXYu3cv5HI5XnzxRSQnJ+PatWtGzyUiIjFjqSbasubr7jGxuJB9EneP6dG2hhGR1ayKePbt24empiZs3rwZf/nLX7B69Wq89dZbWLhwIb788kuoVCr8+uuvSE9Px7Fjx7B161asWrUKr776KgAYPZeIiAwZmx4UTTtaGH31iQnAxsUT0S8uqI0tIyJrWRV8xcbGQqFQQKlUoqqqCnK5HOnp6Rg+fDgAYPz48Th06BBSU1MxduxYSCQSdOvWDQqFAiUlJUbPJSIiQzIjswLe7tpJi+z8SouvyV2ORPZl1bSjp6cn8vLyMG3aNJSWlmLDhg1ISUkRPtBeXl6orKxEVVUV/P39hedpjqtUKoNzWxMQ4Am5Xo2zrigkhOs0rMF+sw77zTId3V+a61c1KoVjq7ak2ez1O5Izt92e2G/Ws2ffWRV8ffrppxg7diwWLVqEGzdu4OGHH0ZjY6PweHV1NXx9feHt7Y3q6mrRcR8fH9H6Ls25rSktrbGmqZ1KSIgPCgst/5Xb1bHfrMN+s4wt+ktz/ca6xhYfdzZ8r1mH/WY9W/RdS8GdVdOOvr6+8PFRX9TPzw9NTU1ISkrC0aNHAQD79+/H0KFDMXjwYBw4cABKpRLXr1+HUqlEYGCg0XOJiMg83h6sDEfkzKz6BM+fPx8vvvgi5syZg8bGRjz//PPo168fli5dilWrViEuLg5TpkyBTCbD0KFDMWvWLCiVSixbtgwAsHjxYoNziYjIPC5cgkHk1KwKvry8vLBmzRqD45s2bTI4tmDBAixYsEB0LDY21ui5RERERJ0dk2sRERER2RCDLyIiIiIbYvBFROSE4ru3vkuciBwTgy8iIidUUlFv7yYQkZUYfBEROaHSSgZfRM6KyWKIiBzc9JExqDaRWJWInA+DLyIiB/fAhHh7N4GI2hGnHYmIiIhsiMEXERERkQ0x+CIi6gQSYwLs3QQiMhODLyIiJ+fv7Yq/zhpg72YQkZkYfBERObnwQE/IpPxzTuQs+GklIiIisiEGX0REREQ2xOCLiMgJDewZbO8mEJGVGHwRETmhRoXS3k0gIisx+CIickIlFXXCbYlEYseWEJGlGHwRETmhG8U19m4CEVmJwRcRERGRDTH4IiJyQs/M6G/vJhCRlRh8ERE5ocEJIfjT9D4AgLHJEXZuDRFZQm7vBhARkXXGJXfDkIRQeLrzTzmRM+HIFxGRE2PgReR8GHwRERER2RCDLyIiIiIbYvBFREREZEMMvoiIiIhsiMEXERERkQ0x+CIiIiKyIQZfRERERDbE4IuIiIjIhhh8EREREdkQgy8iIiIiG2LwRURERGRDEpVKpbJ3I4iIiIi6Co58EREREdkQgy8iIiIiG2LwRURERGRDDL6IiIiIbIjBFxEREZENMfgiIiIisiEGXw6KGUDIVvhesw77zXyNjY24fv26vZvhdBobG5GSkmLvZnQKjvZ5ZfDlILZs2YIVK1Zg27ZtAACJRGLfBjkBzYcpLS0Nubm5dm6Nc7ly5QpeeeUVAHyvmWvz5s145ZVXsHXrVgDsN3Nt27YNc+fOxd69e+3dFKeye/du3HvvvcLnlCzj6N+pDL7sSKlUQqVSYcOGDdi3bx/uu+8+7Ny5E5988om9m+YUNB+m5cuX4/Dhw6ivr7dzixyb7i+/K1euYOvWrTh+/DgA9XuRDGn6ZdOmTThw4ABmzJiB7du346uvvgLgeL+mHYVSqURDQwOWL1+Oo0eP4sMPP8ScOXOE/mK/mXb9+nU8/fTT2LlzJx588EHce++9AICmpib7NswJONN3KoMvO6mqqoJKpYJEIkFhYSEmTZqE3r17Y9GiRfj4449x8eJFezfRYdXV1Ql/iH788UfcvHkTJ06cQEZGhp1b5riqqqqE2wUFBUhJScHs2bOFX9VSKf8U6NPtsytXrmDSpElITk7GLbfcAqlUisbGRof7Ne0INP3m6uoKuVwOX19ffPfdd3jsscfw9NNP4+rVq+w3IzT91tTUhPnz5+Odd95BcnIyjhw5AgCQy+X2bJ7Dc7bvVP7FtYP3338fCxcuxLvvvouUlBRER0ejsrISDQ0N6NWrF2JiYrBnzx4A/IWoLy8vD++88w5OnToFAOjevTs2bdqEoKAgpKSkoKyszK7tc0Sa99vq1auRnp6O0NBQjB8/HkuXLkVAQAA+/vhjABz90qXpszVr1iAtLQ2PP/447r33XqSkpODjjz9Gamoqli1bBoVCYe+mOhRNv61atQoZGRl44okn8Msvv6CyshIfffQREhMT8eWXX6K6utreTXUo77//Pp577jmsWbMGZWVlGD58OAD1mq9BgwYB4OezJc74ncrgy8aOHTuG9PR0vPXWWwgKCsK+fftQXl6O0tJSLFmyBI8++igmT56MvXv3oqioiL8Q9Zw4cQJ79+5FWloaKisrER8fj9jYWNx+++04d+4crly5Yu8mOhTd91tISAi++eYbpKam4pZbbgEAvPjii9i0aRNqamoglUod5g+TPen2WXBwMLZv346cnBxIpVLExcVh586dePvtt3HmzBmH+zVtT7r9Fh4ejs8//1xYWzh9+nQAwLPPPoujR4+ioKDAzq11HJp+W7FiBYKCgrBt2zYcPnwYgDrg+v333wFwdNoUZ/1O5f9NGzt79iyGDh2KkJAQTJ8+HT179kRhYSGefvppTJs2DQsXLsRdd92FoUOHIjg42N7NdTh5eXmYPn06qqqqkJKSAl9fXwBAcnIy4uLisG/fPu6q0qH7fps2bRr69u2Lbdu2Cb+iExMTMWjQICxZsgSA4y1KtQf9z2hSUhJ27NgBAFAoFJDJZCgsLETPnj3h4eFh59Y6Dv33Wv/+/bFjxw6MGTMGLi4uuHnzJkpKSpCYmCh8bsmw35KSkvDTTz9BqVRi1KhRCA8Px+7duwE4zqiNI3HW71QGXzai+bJLSkoSFusGBwejX79+cHNzw5kzZ+Dv74/PPvsMTz/9NHr16mXP5jokpVKJu+66C8888wx8fHxw7tw55OTkCI/fcccdqKystGMLHYex91tQUBAGDhwIDw8PYdoWUG9YmDp1qj2a6VBa6jNPT0/s27cPv/32G5577jksXLgQEyZMQGxsrD2b7BBM9duAAQPg6emJgwcP4qeffsKiRYvw9NNPY/To0QgKCrJnkx1Ca5/RkydPAgCGDBmCCxcuCOuZSM3Zv1O5gq+DfPPNN3Bzc8PQoUMREREBlUoFpVKJkSNHolevXvjXv/6F559/HnFxcaivr4ePjw969+6N2tpajB49uksvrtTvO81og1QqRUREBKRSKcaMGYMffvgBJ0+eRFRUFAAgMjISL7/8cpf8A7V161a4uLhg7NixCA4ObvH9VltbCy8vLwDqkRwvLy9hWqgrsbTPwsPDccstt6Bfv37o06dPl/2MWttvkyZNQlxcHGQymb3/CXZhSb/V1NTA29sbADBp0iR069atS/5d02VJ/znDdypHvtpZaWkpHn74YaSlpaGoqAjvvfcejh8/LgQPV65cwbx587Bjxw4cPnwYBw8exLVr19DY2AgAGD9+vMO9SWylpb4D1Ou98vLyAAC9e/dGt27dcPbsWdHoV1f6A6VSqVBeXo7HH38caWlpyMzMxLp163DixIlW32+aheJd7YvQ2j7LysoSPqP9+vXrcp/R9ui3Xr168f1mZr9lZ2cLO7ojIyO77HqvtvyNc/jvVBW1q4yMDNXSpUuF+5s2bVItWLBAVV1drXr11VdVs2fPVtXV1al27dqlev/991UPP/yw6vDhw3ZsseNore/++Mc/qrKzs4XHi4uLVVlZWfZoqt3V19erVCqVqqSkRLV8+XKVSqVSNTU1qb755hvVk08+yfebEewz67DfrMN+a5vO3n8SlYor+Kylap6D37x5Mzw8PHDPPffg8OHDWLNmDT7//HO4uLhg+/bt+P777zFq1CiMHDkS/fr1s3ezHQL7zjp1dXV455130NDQgEGDBmHw4MFYvnw53nvvPbi7u6O6uhrLly9HTEwMJkyYgKSkJHs32e7YZ9Zhv1mH/dY2XaX/uuZYZjvRTHEdOnQIH3zwgbA7JTg4GG+++SZWr16NAwcOYNSoUXB3dxeCB+YGYt9Zo6ysDC+99BL8/Pwwd+5crFixAm5ubvDx8cGmTZsAAG5ubhg5ciRkMpnwR4l9xj6zFPvNOuy3tulK/cfgywqFhYXC7ZSUFAQEBCA8PBzLly8HALzyyiuYOXMm5HI5li1bBg8PD4SEhAjP6WrrHnSx7yyn6TOlUonS0lI89NBD6NWrF6ZOnYr09HQsWLAAP/zwAy5dugS5XI7r168jICBAeD77jH1mLvabddhvbdMV+88BV6E5rps3b2Lt2rUoLi7GpEmTMH78eMTFxWHevHno1q0bJk+ejEcffRRRUVEoKytDZGQk3nzzTRQWFuKFF16wd/Ptin1nOd0+mzx5MuLj4/H3v/9dyJFUWFiIkJAQxMfH4+6778aWLVuQk5ODxsZGPPfcc3ZuvX2wz6zDfrMO+61tunL/cc2XBd5//300NjYKxXVLS0vx17/+Vdi2v3r1aly8eBHr169HY2Mjrl69ijNnzuCBBx6wc8vtj31nOd0++/7771FcXIxFixbBy8sLx48fx/r167Fx40YAQHl5OWQyGY4fP44JEybYt+F2xD6zDvvNOuy3tunK/cfgqxXffvstjh07hqioKOTl5eHpp59GVFQUrl27hq+//hphYWF4+OGHhfOHDx+OFStWYNKkSXZstWNg31nOVJ9lZ2dj8+bNCA0Nxfz58/HNN99AKpUiKCgIa9euxYIFC4SSQV0N+8w67DfrsN/ahv2nxjVfLXjnnXewf/9+zJs3DxcvXsR//vMfbN68GQAQHh6O0aNH4/r166JizqtWrRKSfnZl7DvLtdRnYWFhQp8BwK5du/DGG2/gl19+wSuvvNKp/ihZgn1mHfabddhvbcP+0+KarxZUVlZi1qxZ6Nu3Lx566CGEhobixx9/xJ133onExEQEBQWhvr4enp6eQuqEsWPH2rvZDoF9Zzlz+qyurg6VlZXo378/ZsyYgSlTpti72XbFPrMO+8067Le2Yf9pMfgyQalU4vbbb0dycjIA4KeffsKtt96KhIQEvPHGG3jttddw6NAhlJWVQalUdqnM6q1h31nOkj7z9PTEM888Y+cW2x/7zDrsN+uw39qG/SfGNV9mqKqqwvz587F+/XqEhIRg/fr1KC8vR1FRERYvXixKhUBi7DvLsc8sxz6zDvvNOuy3tmH/ceTLLPn5+Rg9ejQqKyvx+uuvo1evXli0aBFcXFzs3TSHx76zHPvMcuwz67DfrMN+axv2H4Mvs6SkpOCDDz5Aeno67rnnHtx99932bpLTYN9Zjn1mOfaZddhv1mG/tQ37j9OOZvn2229RWFiIRx55BK6urvZujlNh31mOfWY59pl12G/WYb+1DfuPwZdZNLvxyHLsO8uxzyzHPrMO+8067Le2Yf8x+CIiIiKyKSZZJSIiIrIhBl9ERERENsTgi4iIiMiGGHwRERER2RDzfBGRw8rNzcXUqVMRHx8PAKirq0Pv3r2xbNkyBAcHm3ze3Llz8fnnn5v9OuvXr8fOnTsBABcuXECfPn0AAFOnTkVubi5mz56N/v37t+FfQkSkxd2OROSwcnNzMW/ePPz2228A1FvUV61ahdTUVHz55Zcmn9e7d29cvHjRqtdsy3OJiMzBkS8ichoSiQQLFizAmDFjcOHCBWzatAmXL19GUVERYmNjsW7dOrzzzjsAgAcffBBbt27F/v378e6776KpqQmRkZF47bXXEBAQYPZrzp07Vyjyu2HDBqhUKmRnZ2PKlCnw8fHB7t27AQAffPABgoOD2/x6RNT5cc0XETkVV1dXxMTEYPfu3XBxccHXX3+NXbt2ob6+Hvv27cNLL70EANi6dStKSkqwcuVKbNy4Edu2bcPYsWOF4MwaaWlpeOutt7Bjxw5s3rwZgYGB+O6779C7d2/s2LGj3V+PiDonjnwRkdORSCRISkpCVFQUvvjiC1y9ehVZWVmoqakRnZeWloYbN25g3rx5AAClUgk/Pz+rXzchIQEREREAgICAAIwaNQoA0K1bN1RUVLT76xFR58Tgi4icSkNDAzIzM5GTk4M1a9Zg3rx5mDFjBkpLS6G/hFWhUGDw4MHYsGEDAKC+vh7V1dU4c+aMMELWr18/vPHGG2a9touLi+i+TCYz6/WIiHRx2pGInIZSqcTatWsxYMAA5OTkYNq0abj//vsRHByMlJQUKBQKAOqgqKmpCQMGDMCpU6eQmZkJAHj//ffx9ttvo3///ti+fTu2b99uduBlDlOvR0SkiyNfROTQCgoKcM899wBQB1+JiYlYuXIl8vPz8cILL2Dnzp1wdXXFwIEDkZubCwC49dZbcc899+C7777Dm2++iYULF0KpVCIsLAz//Oc/O6ytISEhNn09InJOTDVBREREZEOcdiQiIiKyIQZfRERERDbE4IuIiIjIhhh8EREREdkQgy8iIiIiG2LwRURERGRDDL6IiIiIbIjBFxEREZEN/T94/6raOmx4mQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resam_data.Price.plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f6ccad1",
   "metadata": {},
   "source": [
    "### Preparing Features & Labels Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ee637d",
   "metadata": {},
   "source": [
    "shift(1) : 向下移动一格（首行添加为 Nan）  [link](https://www.cnblogs.com/iamxyq/p/6283334.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8ebb7d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>RET</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:16:00+05:30</th>\n",
       "      <td>11078.30</td>\n",
       "      <td>216150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:17:00+05:30</th>\n",
       "      <td>11064.55</td>\n",
       "      <td>139350.0</td>\n",
       "      <td>-0.001242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:18:00+05:30</th>\n",
       "      <td>11070.40</td>\n",
       "      <td>127950.0</td>\n",
       "      <td>0.000529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:19:00+05:30</th>\n",
       "      <td>11078.05</td>\n",
       "      <td>84000.0</td>\n",
       "      <td>0.000691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:20:00+05:30</th>\n",
       "      <td>11082.50</td>\n",
       "      <td>104700.0</td>\n",
       "      <td>0.000402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price    Volume       RET\n",
       "Date-Time                                              \n",
       "2019-08-01 09:16:00+05:30  11078.30  216150.0       NaN\n",
       "2019-08-01 09:17:00+05:30  11064.55  139350.0 -0.001242\n",
       "2019-08-01 09:18:00+05:30  11070.40  127950.0  0.000529\n",
       "2019-08-01 09:19:00+05:30  11078.05   84000.0  0.000691\n",
       "2019-08-01 09:20:00+05:30  11082.50  104700.0  0.000402"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resam_data['RET'] = np.log(resam_data['Price'] / resam_data['Price'].shift(1))  # Logarithmic Returns\n",
    "resam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b7b1b0e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.447942968243341"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(11064/127950)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "74ec5be5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>RET</th>\n",
       "      <th>SMA1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-22 10:12:00+05:30</th>\n",
       "      <td>11672.90</td>\n",
       "      <td>33300.0</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>11560.95670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-20 13:36:00+05:30</th>\n",
       "      <td>12267.70</td>\n",
       "      <td>17175.0</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>12240.97930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-07 12:48:00+05:30</th>\n",
       "      <td>11240.00</td>\n",
       "      <td>5025.0</td>\n",
       "      <td>-0.000089</td>\n",
       "      <td>11324.30640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-13 10:52:00+05:30</th>\n",
       "      <td>12109.05</td>\n",
       "      <td>13875.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11964.27185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07 10:52:00+05:30</th>\n",
       "      <td>12158.60</td>\n",
       "      <td>21150.0</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>12192.62615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price   Volume       RET         SMA1\n",
       "Date-Time                                                          \n",
       "2019-10-22 10:12:00+05:30  11672.90  33300.0  0.000274  11560.95670\n",
       "2019-12-20 13:36:00+05:30  12267.70  17175.0  0.000175  12240.97930\n",
       "2019-10-07 12:48:00+05:30  11240.00   5025.0 -0.000089  11324.30640\n",
       "2019-12-13 10:52:00+05:30  12109.05  13875.0  0.000000  11964.27185\n",
       "2020-01-07 10:52:00+05:30  12158.60  21150.0  0.000078  12192.62615"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "window = 1000\n",
    "\n",
    "resam_data['SMA1'] = resam_data['Price'].rolling(window).mean()  # simple moving average\n",
    "resam_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c4c1277c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>RET</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:16:00+05:30</th>\n",
       "      <td>11078.30</td>\n",
       "      <td>216150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:17:00+05:30</th>\n",
       "      <td>11064.55</td>\n",
       "      <td>139350.0</td>\n",
       "      <td>-0.001242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:18:00+05:30</th>\n",
       "      <td>11070.40</td>\n",
       "      <td>127950.0</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:19:00+05:30</th>\n",
       "      <td>11078.05</td>\n",
       "      <td>84000.0</td>\n",
       "      <td>0.000691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:20:00+05:30</th>\n",
       "      <td>11082.50</td>\n",
       "      <td>104700.0</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price    Volume       RET  SMA1  SMA2\n",
       "Date-Time                                                          \n",
       "2019-08-01 09:16:00+05:30  11078.30  216150.0       NaN   NaN   NaN\n",
       "2019-08-01 09:17:00+05:30  11064.55  139350.0 -0.001242   NaN   NaN\n",
       "2019-08-01 09:18:00+05:30  11070.40  127950.0  0.000529   NaN   NaN\n",
       "2019-08-01 09:19:00+05:30  11078.05   84000.0  0.000691   NaN   NaN\n",
       "2019-08-01 09:20:00+05:30  11082.50  104700.0  0.000402   NaN   NaN"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resam_data['SMA2'] = resam_data['Price'].rolling(2*window).mean()\n",
    "resam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "91af4367",
   "metadata": {},
   "outputs": [],
   "source": [
    "resam_data['MOM'] = resam_data['RET'].rolling(window).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f6a5b3b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "resam_data['VOL'] = resam_data['RET'].rolling(window).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2291d105",
   "metadata": {},
   "outputs": [],
   "source": [
    "resam_data['MIN'] = resam_data['Price'].rolling(window).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "04db67af",
   "metadata": {},
   "outputs": [],
   "source": [
    "resam_data['MAX'] = resam_data['Price'].rolling(window).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3e63a13d",
   "metadata": {},
   "outputs": [],
   "source": [
    "resam_data.dropna(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b55ce54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "resam_data['D'] = np.sign(resam_data['RET'])  # labels\n",
    "resam_data['D'] = resam_data['D'].astype(int)  # 转为 int 型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "caa2dcd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>RET</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>MOM</th>\n",
       "      <th>VOL</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:15:00+05:30</th>\n",
       "      <td>10902.30</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>10935.20145</td>\n",
       "      <td>10948.615325</td>\n",
       "      <td>3.073218e-07</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.80</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:16:00+05:30</th>\n",
       "      <td>10908.00</td>\n",
       "      <td>15750.0</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>10935.21605</td>\n",
       "      <td>10948.530175</td>\n",
       "      <td>1.339364e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.80</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:17:00+05:30</th>\n",
       "      <td>10905.55</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>-0.000225</td>\n",
       "      <td>10935.22715</td>\n",
       "      <td>10948.450675</td>\n",
       "      <td>1.018349e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.80</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:18:00+05:30</th>\n",
       "      <td>10906.00</td>\n",
       "      <td>5325.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>10935.23925</td>\n",
       "      <td>10948.368475</td>\n",
       "      <td>1.110097e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.80</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:19:00+05:30</th>\n",
       "      <td>10902.30</td>\n",
       "      <td>15600.0</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>10935.25055</td>\n",
       "      <td>10948.280600</td>\n",
       "      <td>1.037016e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.80</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31 15:27:00+05:30</th>\n",
       "      <td>11366.00</td>\n",
       "      <td>91125.0</td>\n",
       "      <td>-0.000501</td>\n",
       "      <td>11608.78805</td>\n",
       "      <td>11551.542500</td>\n",
       "      <td>-1.978365e-05</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>11357.90</td>\n",
       "      <td>11791.8</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31 15:28:00+05:30</th>\n",
       "      <td>11357.05</td>\n",
       "      <td>161625.0</td>\n",
       "      <td>-0.000788</td>\n",
       "      <td>11608.54995</td>\n",
       "      <td>11551.479225</td>\n",
       "      <td>-2.074821e-05</td>\n",
       "      <td>0.000575</td>\n",
       "      <td>11357.05</td>\n",
       "      <td>11791.8</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31 15:29:00+05:30</th>\n",
       "      <td>11355.40</td>\n",
       "      <td>208425.0</td>\n",
       "      <td>-0.000145</td>\n",
       "      <td>11608.31085</td>\n",
       "      <td>11551.416550</td>\n",
       "      <td>-2.083745e-05</td>\n",
       "      <td>0.000575</td>\n",
       "      <td>11355.40</td>\n",
       "      <td>11791.8</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31 15:30:00+05:30</th>\n",
       "      <td>11359.80</td>\n",
       "      <td>155250.0</td>\n",
       "      <td>0.000387</td>\n",
       "      <td>11608.07615</td>\n",
       "      <td>11551.357650</td>\n",
       "      <td>-2.045004e-05</td>\n",
       "      <td>0.000575</td>\n",
       "      <td>11355.40</td>\n",
       "      <td>11791.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31 15:31:00+05:30</th>\n",
       "      <td>11359.80</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11607.84110</td>\n",
       "      <td>11551.295950</td>\n",
       "      <td>-2.048023e-05</td>\n",
       "      <td>0.000575</td>\n",
       "      <td>11355.40</td>\n",
       "      <td>11791.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97945 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price    Volume       RET         SMA1  \\\n",
       "Date-Time                                                              \n",
       "2019-08-08 11:15:00+05:30  10902.30    4200.0  0.000023  10935.20145   \n",
       "2019-08-08 11:16:00+05:30  10908.00   15750.0  0.000523  10935.21605   \n",
       "2019-08-08 11:17:00+05:30  10905.55    9300.0 -0.000225  10935.22715   \n",
       "2019-08-08 11:18:00+05:30  10906.00    5325.0  0.000041  10935.23925   \n",
       "2019-08-08 11:19:00+05:30  10902.30   15600.0 -0.000339  10935.25055   \n",
       "...                             ...       ...       ...          ...   \n",
       "2020-08-31 15:27:00+05:30  11366.00   91125.0 -0.000501  11608.78805   \n",
       "2020-08-31 15:28:00+05:30  11357.05  161625.0 -0.000788  11608.54995   \n",
       "2020-08-31 15:29:00+05:30  11355.40  208425.0 -0.000145  11608.31085   \n",
       "2020-08-31 15:30:00+05:30  11359.80  155250.0  0.000387  11608.07615   \n",
       "2020-08-31 15:31:00+05:30  11359.80    3000.0  0.000000  11607.84110   \n",
       "\n",
       "                                   SMA2           MOM       VOL       MIN  \\\n",
       "Date-Time                                                                   \n",
       "2019-08-08 11:15:00+05:30  10948.615325  3.073218e-07  0.000495  10847.80   \n",
       "2019-08-08 11:16:00+05:30  10948.530175  1.339364e-06  0.000495  10847.80   \n",
       "2019-08-08 11:17:00+05:30  10948.450675  1.018349e-06  0.000495  10847.80   \n",
       "2019-08-08 11:18:00+05:30  10948.368475  1.110097e-06  0.000495  10847.80   \n",
       "2019-08-08 11:19:00+05:30  10948.280600  1.037016e-06  0.000495  10847.80   \n",
       "...                                 ...           ...       ...       ...   \n",
       "2020-08-31 15:27:00+05:30  11551.542500 -1.978365e-05  0.000574  11357.90   \n",
       "2020-08-31 15:28:00+05:30  11551.479225 -2.074821e-05  0.000575  11357.05   \n",
       "2020-08-31 15:29:00+05:30  11551.416550 -2.083745e-05  0.000575  11355.40   \n",
       "2020-08-31 15:30:00+05:30  11551.357650 -2.045004e-05  0.000575  11355.40   \n",
       "2020-08-31 15:31:00+05:30  11551.295950 -2.048023e-05  0.000575  11355.40   \n",
       "\n",
       "                               MAX  D  \n",
       "Date-Time                              \n",
       "2019-08-08 11:15:00+05:30  11046.1  1  \n",
       "2019-08-08 11:16:00+05:30  11046.1  1  \n",
       "2019-08-08 11:17:00+05:30  11046.1 -1  \n",
       "2019-08-08 11:18:00+05:30  11046.1  1  \n",
       "2019-08-08 11:19:00+05:30  11046.1 -1  \n",
       "...                            ... ..  \n",
       "2020-08-31 15:27:00+05:30  11791.8 -1  \n",
       "2020-08-31 15:28:00+05:30  11791.8 -1  \n",
       "2020-08-31 15:29:00+05:30  11791.8 -1  \n",
       "2020-08-31 15:30:00+05:30  11791.8  1  \n",
       "2020-08-31 15:31:00+05:30  11791.8  0  \n",
       "\n",
       "[97945 rows x 10 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resam_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ea429bdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Price', 'Volume', 'RET', 'SMA1', 'SMA2', 'MOM', 'VOL', 'MIN', 'MAX']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['Price', 'Volume', 'RET', 'SMA1', 'SMA2', 'MOM', 'VOL', 'MIN', 'MAX'][:]\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5811577",
   "metadata": {},
   "source": [
    "### Simple strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1546dc4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>RET</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>MOM</th>\n",
       "      <th>VOL</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>D</th>\n",
       "      <th>POS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:15:00+05:30</th>\n",
       "      <td>10902.30</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>10935.20145</td>\n",
       "      <td>10948.615325</td>\n",
       "      <td>3.073218e-07</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:16:00+05:30</th>\n",
       "      <td>10908.00</td>\n",
       "      <td>15750.0</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>10935.21605</td>\n",
       "      <td>10948.530175</td>\n",
       "      <td>1.339364e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:17:00+05:30</th>\n",
       "      <td>10905.55</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>-0.000225</td>\n",
       "      <td>10935.22715</td>\n",
       "      <td>10948.450675</td>\n",
       "      <td>1.018349e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:18:00+05:30</th>\n",
       "      <td>10906.00</td>\n",
       "      <td>5325.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>10935.23925</td>\n",
       "      <td>10948.368475</td>\n",
       "      <td>1.110097e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:19:00+05:30</th>\n",
       "      <td>10902.30</td>\n",
       "      <td>15600.0</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>10935.25055</td>\n",
       "      <td>10948.280600</td>\n",
       "      <td>1.037016e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price   Volume       RET         SMA1  \\\n",
       "Date-Time                                                             \n",
       "2019-08-08 11:15:00+05:30  10902.30   4200.0  0.000023  10935.20145   \n",
       "2019-08-08 11:16:00+05:30  10908.00  15750.0  0.000523  10935.21605   \n",
       "2019-08-08 11:17:00+05:30  10905.55   9300.0 -0.000225  10935.22715   \n",
       "2019-08-08 11:18:00+05:30  10906.00   5325.0  0.000041  10935.23925   \n",
       "2019-08-08 11:19:00+05:30  10902.30  15600.0 -0.000339  10935.25055   \n",
       "\n",
       "                                   SMA2           MOM       VOL      MIN  \\\n",
       "Date-Time                                                                  \n",
       "2019-08-08 11:15:00+05:30  10948.615325  3.073218e-07  0.000495  10847.8   \n",
       "2019-08-08 11:16:00+05:30  10948.530175  1.339364e-06  0.000495  10847.8   \n",
       "2019-08-08 11:17:00+05:30  10948.450675  1.018349e-06  0.000495  10847.8   \n",
       "2019-08-08 11:18:00+05:30  10948.368475  1.110097e-06  0.000495  10847.8   \n",
       "2019-08-08 11:19:00+05:30  10948.280600  1.037016e-06  0.000495  10847.8   \n",
       "\n",
       "                               MAX  D  POS  \n",
       "Date-Time                                   \n",
       "2019-08-08 11:15:00+05:30  11046.1  1   -1  \n",
       "2019-08-08 11:16:00+05:30  11046.1  1   -1  \n",
       "2019-08-08 11:17:00+05:30  11046.1 -1   -1  \n",
       "2019-08-08 11:18:00+05:30  11046.1  1   -1  \n",
       "2019-08-08 11:19:00+05:30  11046.1 -1   -1  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resam_data['POS'] = np.where(resam_data['SMA1'] > resam_data['SMA2'], 1, -1)\n",
    "resam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e8a8a03b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>RET</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>MOM</th>\n",
       "      <th>VOL</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>D</th>\n",
       "      <th>POS</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:15:00+05:30</th>\n",
       "      <td>10902.30</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>10935.20145</td>\n",
       "      <td>10948.615325</td>\n",
       "      <td>3.073218e-07</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:16:00+05:30</th>\n",
       "      <td>10908.00</td>\n",
       "      <td>15750.0</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>10935.21605</td>\n",
       "      <td>10948.530175</td>\n",
       "      <td>1.339364e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.000523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:17:00+05:30</th>\n",
       "      <td>10905.55</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>-0.000225</td>\n",
       "      <td>10935.22715</td>\n",
       "      <td>10948.450675</td>\n",
       "      <td>1.018349e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:18:00+05:30</th>\n",
       "      <td>10906.00</td>\n",
       "      <td>5325.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>10935.23925</td>\n",
       "      <td>10948.368475</td>\n",
       "      <td>1.110097e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.000041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:19:00+05:30</th>\n",
       "      <td>10902.30</td>\n",
       "      <td>15600.0</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>10935.25055</td>\n",
       "      <td>10948.280600</td>\n",
       "      <td>1.037016e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price   Volume       RET         SMA1  \\\n",
       "Date-Time                                                             \n",
       "2019-08-08 11:15:00+05:30  10902.30   4200.0  0.000023  10935.20145   \n",
       "2019-08-08 11:16:00+05:30  10908.00  15750.0  0.000523  10935.21605   \n",
       "2019-08-08 11:17:00+05:30  10905.55   9300.0 -0.000225  10935.22715   \n",
       "2019-08-08 11:18:00+05:30  10906.00   5325.0  0.000041  10935.23925   \n",
       "2019-08-08 11:19:00+05:30  10902.30  15600.0 -0.000339  10935.25055   \n",
       "\n",
       "                                   SMA2           MOM       VOL      MIN  \\\n",
       "Date-Time                                                                  \n",
       "2019-08-08 11:15:00+05:30  10948.615325  3.073218e-07  0.000495  10847.8   \n",
       "2019-08-08 11:16:00+05:30  10948.530175  1.339364e-06  0.000495  10847.8   \n",
       "2019-08-08 11:17:00+05:30  10948.450675  1.018349e-06  0.000495  10847.8   \n",
       "2019-08-08 11:18:00+05:30  10948.368475  1.110097e-06  0.000495  10847.8   \n",
       "2019-08-08 11:19:00+05:30  10948.280600  1.037016e-06  0.000495  10847.8   \n",
       "\n",
       "                               MAX  D  POS         S  \n",
       "Date-Time                                             \n",
       "2019-08-08 11:15:00+05:30  11046.1  1   -1       NaN  \n",
       "2019-08-08 11:16:00+05:30  11046.1  1   -1 -0.000523  \n",
       "2019-08-08 11:17:00+05:30  11046.1 -1   -1  0.000225  \n",
       "2019-08-08 11:18:00+05:30  11046.1  1   -1 -0.000041  \n",
       "2019-08-08 11:19:00+05:30  11046.1 -1   -1  0.000339  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resam_data['S'] = resam_data['POS'].shift(1) * resam_data['RET'] \n",
    "resam_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa6884df",
   "metadata": {},
   "source": [
    "DataFrame.apply() 函数则会遍历每一个元素，对元素运行指定的 function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d15eda36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>RET</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>MOM</th>\n",
       "      <th>VOL</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>D</th>\n",
       "      <th>POS</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:15:00+05:30</th>\n",
       "      <td>10902.30</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>10935.20145</td>\n",
       "      <td>10948.615325</td>\n",
       "      <td>3.073218e-07</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:16:00+05:30</th>\n",
       "      <td>10908.00</td>\n",
       "      <td>15750.0</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>10935.21605</td>\n",
       "      <td>10948.530175</td>\n",
       "      <td>1.339364e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.000523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:17:00+05:30</th>\n",
       "      <td>10905.55</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>-0.000225</td>\n",
       "      <td>10935.22715</td>\n",
       "      <td>10948.450675</td>\n",
       "      <td>1.018349e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:18:00+05:30</th>\n",
       "      <td>10906.00</td>\n",
       "      <td>5325.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>10935.23925</td>\n",
       "      <td>10948.368475</td>\n",
       "      <td>1.110097e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.000041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:19:00+05:30</th>\n",
       "      <td>10902.30</td>\n",
       "      <td>15600.0</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>10935.25055</td>\n",
       "      <td>10948.280600</td>\n",
       "      <td>1.037016e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price   Volume       RET         SMA1  \\\n",
       "Date-Time                                                             \n",
       "2019-08-08 11:15:00+05:30  10902.30   4200.0  0.000023  10935.20145   \n",
       "2019-08-08 11:16:00+05:30  10908.00  15750.0  0.000523  10935.21605   \n",
       "2019-08-08 11:17:00+05:30  10905.55   9300.0 -0.000225  10935.22715   \n",
       "2019-08-08 11:18:00+05:30  10906.00   5325.0  0.000041  10935.23925   \n",
       "2019-08-08 11:19:00+05:30  10902.30  15600.0 -0.000339  10935.25055   \n",
       "\n",
       "                                   SMA2           MOM       VOL      MIN  \\\n",
       "Date-Time                                                                  \n",
       "2019-08-08 11:15:00+05:30  10948.615325  3.073218e-07  0.000495  10847.8   \n",
       "2019-08-08 11:16:00+05:30  10948.530175  1.339364e-06  0.000495  10847.8   \n",
       "2019-08-08 11:17:00+05:30  10948.450675  1.018349e-06  0.000495  10847.8   \n",
       "2019-08-08 11:18:00+05:30  10948.368475  1.110097e-06  0.000495  10847.8   \n",
       "2019-08-08 11:19:00+05:30  10948.280600  1.037016e-06  0.000495  10847.8   \n",
       "\n",
       "                               MAX  D  POS         S  \n",
       "Date-Time                                             \n",
       "2019-08-08 11:15:00+05:30  11046.1  1   -1       NaN  \n",
       "2019-08-08 11:16:00+05:30  11046.1  1   -1 -0.000523  \n",
       "2019-08-08 11:17:00+05:30  11046.1 -1   -1  0.000225  \n",
       "2019-08-08 11:18:00+05:30  11046.1  1   -1 -0.000041  \n",
       "2019-08-08 11:19:00+05:30  11046.1 -1   -1  0.000339  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resam_data[['RET', 'S']].sum().apply(np.exp)\n",
    "resam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "06a8e124",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date-Time'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resam_data[['RET', 'S']].cumsum().apply(np.exp).plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d84d299",
   "metadata": {},
   "source": [
    "### Lagging the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f71ce90d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = 3\n",
    "\n",
    "cols = []\n",
    "for f in features:\n",
    "    for lag in range(1, lags + 1):\n",
    "        col = f'{f}_lag_{lag}'\n",
    "        resam_data[col] = resam_data[f].shift(lag)\n",
    "        cols.append(col)\n",
    "        \n",
    "resam_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7da4ccec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>RET</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>MOM</th>\n",
       "      <th>VOL</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>D</th>\n",
       "      <th>...</th>\n",
       "      <th>MOM_lag_3</th>\n",
       "      <th>VOL_lag_1</th>\n",
       "      <th>VOL_lag_2</th>\n",
       "      <th>VOL_lag_3</th>\n",
       "      <th>MIN_lag_1</th>\n",
       "      <th>MIN_lag_2</th>\n",
       "      <th>MIN_lag_3</th>\n",
       "      <th>MAX_lag_1</th>\n",
       "      <th>MAX_lag_2</th>\n",
       "      <th>MAX_lag_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:18:00+05:30</th>\n",
       "      <td>10906.0</td>\n",
       "      <td>5325.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>10935.23925</td>\n",
       "      <td>10948.368475</td>\n",
       "      <td>1.110097e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.073218e-07</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>11046.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:19:00+05:30</th>\n",
       "      <td>10902.3</td>\n",
       "      <td>15600.0</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>10935.25055</td>\n",
       "      <td>10948.280600</td>\n",
       "      <td>1.037016e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.339364e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>11046.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:20:00+05:30</th>\n",
       "      <td>10900.1</td>\n",
       "      <td>10050.0</td>\n",
       "      <td>-0.000202</td>\n",
       "      <td>10935.25715</td>\n",
       "      <td>10948.189400</td>\n",
       "      <td>6.056824e-07</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.018349e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>11046.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:21:00+05:30</th>\n",
       "      <td>10892.7</td>\n",
       "      <td>40050.0</td>\n",
       "      <td>-0.000679</td>\n",
       "      <td>10935.24430</td>\n",
       "      <td>10948.096800</td>\n",
       "      <td>-1.178994e-06</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.110097e-06</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>11046.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:22:00+05:30</th>\n",
       "      <td>10898.0</td>\n",
       "      <td>30450.0</td>\n",
       "      <td>0.000486</td>\n",
       "      <td>10935.24325</td>\n",
       "      <td>10948.006100</td>\n",
       "      <td>-9.634331e-08</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.037016e-06</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>10847.8</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>11046.1</td>\n",
       "      <td>11046.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Price   Volume       RET         SMA1  \\\n",
       "Date-Time                                                            \n",
       "2019-08-08 11:18:00+05:30  10906.0   5325.0  0.000041  10935.23925   \n",
       "2019-08-08 11:19:00+05:30  10902.3  15600.0 -0.000339  10935.25055   \n",
       "2019-08-08 11:20:00+05:30  10900.1  10050.0 -0.000202  10935.25715   \n",
       "2019-08-08 11:21:00+05:30  10892.7  40050.0 -0.000679  10935.24430   \n",
       "2019-08-08 11:22:00+05:30  10898.0  30450.0  0.000486  10935.24325   \n",
       "\n",
       "                                   SMA2           MOM       VOL      MIN  \\\n",
       "Date-Time                                                                  \n",
       "2019-08-08 11:18:00+05:30  10948.368475  1.110097e-06  0.000495  10847.8   \n",
       "2019-08-08 11:19:00+05:30  10948.280600  1.037016e-06  0.000495  10847.8   \n",
       "2019-08-08 11:20:00+05:30  10948.189400  6.056824e-07  0.000495  10847.8   \n",
       "2019-08-08 11:21:00+05:30  10948.096800 -1.178994e-06  0.000494  10847.8   \n",
       "2019-08-08 11:22:00+05:30  10948.006100 -9.634331e-08  0.000494  10847.8   \n",
       "\n",
       "                               MAX  D  ...     MOM_lag_3  VOL_lag_1  \\\n",
       "Date-Time                              ...                            \n",
       "2019-08-08 11:18:00+05:30  11046.1  1  ...  3.073218e-07   0.000495   \n",
       "2019-08-08 11:19:00+05:30  11046.1 -1  ...  1.339364e-06   0.000495   \n",
       "2019-08-08 11:20:00+05:30  11046.1 -1  ...  1.018349e-06   0.000495   \n",
       "2019-08-08 11:21:00+05:30  11046.1 -1  ...  1.110097e-06   0.000495   \n",
       "2019-08-08 11:22:00+05:30  11046.1  1  ...  1.037016e-06   0.000494   \n",
       "\n",
       "                           VOL_lag_2  VOL_lag_3  MIN_lag_1  MIN_lag_2  \\\n",
       "Date-Time                                                               \n",
       "2019-08-08 11:18:00+05:30   0.000495   0.000495    10847.8    10847.8   \n",
       "2019-08-08 11:19:00+05:30   0.000495   0.000495    10847.8    10847.8   \n",
       "2019-08-08 11:20:00+05:30   0.000495   0.000495    10847.8    10847.8   \n",
       "2019-08-08 11:21:00+05:30   0.000495   0.000495    10847.8    10847.8   \n",
       "2019-08-08 11:22:00+05:30   0.000495   0.000495    10847.8    10847.8   \n",
       "\n",
       "                           MIN_lag_3  MAX_lag_1  MAX_lag_2  MAX_lag_3  \n",
       "Date-Time                                                              \n",
       "2019-08-08 11:18:00+05:30    10847.8    11046.1    11046.1    11046.1  \n",
       "2019-08-08 11:19:00+05:30    10847.8    11046.1    11046.1    11046.1  \n",
       "2019-08-08 11:20:00+05:30    10847.8    11046.1    11046.1    11046.1  \n",
       "2019-08-08 11:21:00+05:30    10847.8    11046.1    11046.1    11046.1  \n",
       "2019-08-08 11:22:00+05:30    10847.8    11046.1    11046.1    11046.1  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "16b94b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "792459b3",
   "metadata": {},
   "source": [
    "### Splitting the Data\n",
    "- sequential splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f74ef819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73456"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split = int(len(resam_data) * 0.75)\n",
    "split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d2dbd8d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = resam_data.iloc[:split].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3c8bec65",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = resam_data.iloc[split:].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb0ecd2",
   "metadata": {},
   "source": [
    "- Random Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "31653cf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0,     1,     2, ..., 97939, 97940, 97941])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "ind = np.arange(len(resam_data))\n",
    "ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5d440ed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([58837, 42477, 12854, ..., 79683, 56088, 38408])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.shuffle(ind)\n",
    "ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c9b8a6eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.sort_index(inplace=True)\n",
    "test.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b794979f",
   "metadata": {},
   "source": [
    "### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "514adfb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mu, std = train.mean(), train.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1e1eb942",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ = (train - mu) / std\n",
    "\n",
    "test_ = (test - mu) / std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5746930f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>RET</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>MOM</th>\n",
       "      <th>VOL</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>D</th>\n",
       "      <th>...</th>\n",
       "      <th>MOM_lag_3</th>\n",
       "      <th>VOL_lag_1</th>\n",
       "      <th>VOL_lag_2</th>\n",
       "      <th>VOL_lag_3</th>\n",
       "      <th>MIN_lag_1</th>\n",
       "      <th>MIN_lag_2</th>\n",
       "      <th>MIN_lag_3</th>\n",
       "      <th>MAX_lag_1</th>\n",
       "      <th>MAX_lag_2</th>\n",
       "      <th>MAX_lag_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:18:00+05:30</th>\n",
       "      <td>-0.074366</td>\n",
       "      <td>-0.752518</td>\n",
       "      <td>0.038109</td>\n",
       "      <td>-0.058965</td>\n",
       "      <td>-0.057919</td>\n",
       "      <td>0.092854</td>\n",
       "      <td>-0.317182</td>\n",
       "      <td>0.019517</td>\n",
       "      <td>-0.117892</td>\n",
       "      <td>1.016288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067546</td>\n",
       "      <td>-0.317173</td>\n",
       "      <td>-0.317217</td>\n",
       "      <td>-0.317224</td>\n",
       "      <td>0.019503</td>\n",
       "      <td>0.019489</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>-0.117906</td>\n",
       "      <td>-0.11792</td>\n",
       "      <td>-0.117935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:19:00+05:30</th>\n",
       "      <td>-0.077235</td>\n",
       "      <td>-0.510109</td>\n",
       "      <td>-0.301580</td>\n",
       "      <td>-0.058956</td>\n",
       "      <td>-0.057989</td>\n",
       "      <td>0.090544</td>\n",
       "      <td>-0.317128</td>\n",
       "      <td>0.019517</td>\n",
       "      <td>-0.117892</td>\n",
       "      <td>-0.997210</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100160</td>\n",
       "      <td>-0.317174</td>\n",
       "      <td>-0.317165</td>\n",
       "      <td>-0.317209</td>\n",
       "      <td>0.019503</td>\n",
       "      <td>0.019489</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>-0.117906</td>\n",
       "      <td>-0.11792</td>\n",
       "      <td>-0.117935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:20:00+05:30</th>\n",
       "      <td>-0.078941</td>\n",
       "      <td>-0.641045</td>\n",
       "      <td>-0.178847</td>\n",
       "      <td>-0.058951</td>\n",
       "      <td>-0.058061</td>\n",
       "      <td>0.076915</td>\n",
       "      <td>-0.317141</td>\n",
       "      <td>0.019517</td>\n",
       "      <td>-0.117892</td>\n",
       "      <td>-0.997210</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090015</td>\n",
       "      <td>-0.317120</td>\n",
       "      <td>-0.317166</td>\n",
       "      <td>-0.317157</td>\n",
       "      <td>0.019503</td>\n",
       "      <td>0.019489</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>-0.117906</td>\n",
       "      <td>-0.11792</td>\n",
       "      <td>-0.117935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:21:00+05:30</th>\n",
       "      <td>-0.084680</td>\n",
       "      <td>0.066717</td>\n",
       "      <td>-0.604870</td>\n",
       "      <td>-0.058961</td>\n",
       "      <td>-0.058134</td>\n",
       "      <td>0.020520</td>\n",
       "      <td>-0.318078</td>\n",
       "      <td>0.019517</td>\n",
       "      <td>-0.117892</td>\n",
       "      <td>-0.997210</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092915</td>\n",
       "      <td>-0.317134</td>\n",
       "      <td>-0.317112</td>\n",
       "      <td>-0.317159</td>\n",
       "      <td>0.019503</td>\n",
       "      <td>0.019489</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>-0.117906</td>\n",
       "      <td>-0.11792</td>\n",
       "      <td>-0.117935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 11:22:00+05:30</th>\n",
       "      <td>-0.080570</td>\n",
       "      <td>-0.159767</td>\n",
       "      <td>0.435458</td>\n",
       "      <td>-0.058962</td>\n",
       "      <td>-0.058206</td>\n",
       "      <td>0.054731</td>\n",
       "      <td>-0.318222</td>\n",
       "      <td>0.019517</td>\n",
       "      <td>-0.117892</td>\n",
       "      <td>1.016288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090605</td>\n",
       "      <td>-0.318070</td>\n",
       "      <td>-0.317126</td>\n",
       "      <td>-0.317104</td>\n",
       "      <td>0.019503</td>\n",
       "      <td>0.019489</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>-0.117906</td>\n",
       "      <td>-0.11792</td>\n",
       "      <td>-0.117935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Price    Volume       RET      SMA1      SMA2  \\\n",
       "Date-Time                                                                     \n",
       "2019-08-08 11:18:00+05:30 -0.074366 -0.752518  0.038109 -0.058965 -0.057919   \n",
       "2019-08-08 11:19:00+05:30 -0.077235 -0.510109 -0.301580 -0.058956 -0.057989   \n",
       "2019-08-08 11:20:00+05:30 -0.078941 -0.641045 -0.178847 -0.058951 -0.058061   \n",
       "2019-08-08 11:21:00+05:30 -0.084680  0.066717 -0.604870 -0.058961 -0.058134   \n",
       "2019-08-08 11:22:00+05:30 -0.080570 -0.159767  0.435458 -0.058962 -0.058206   \n",
       "\n",
       "                                MOM       VOL       MIN       MAX         D  \\\n",
       "Date-Time                                                                     \n",
       "2019-08-08 11:18:00+05:30  0.092854 -0.317182  0.019517 -0.117892  1.016288   \n",
       "2019-08-08 11:19:00+05:30  0.090544 -0.317128  0.019517 -0.117892 -0.997210   \n",
       "2019-08-08 11:20:00+05:30  0.076915 -0.317141  0.019517 -0.117892 -0.997210   \n",
       "2019-08-08 11:21:00+05:30  0.020520 -0.318078  0.019517 -0.117892 -0.997210   \n",
       "2019-08-08 11:22:00+05:30  0.054731 -0.318222  0.019517 -0.117892  1.016288   \n",
       "\n",
       "                           ...  MOM_lag_3  VOL_lag_1  VOL_lag_2  VOL_lag_3  \\\n",
       "Date-Time                  ...                                               \n",
       "2019-08-08 11:18:00+05:30  ...   0.067546  -0.317173  -0.317217  -0.317224   \n",
       "2019-08-08 11:19:00+05:30  ...   0.100160  -0.317174  -0.317165  -0.317209   \n",
       "2019-08-08 11:20:00+05:30  ...   0.090015  -0.317120  -0.317166  -0.317157   \n",
       "2019-08-08 11:21:00+05:30  ...   0.092915  -0.317134  -0.317112  -0.317159   \n",
       "2019-08-08 11:22:00+05:30  ...   0.090605  -0.318070  -0.317126  -0.317104   \n",
       "\n",
       "                           MIN_lag_1  MIN_lag_2  MIN_lag_3  MAX_lag_1  \\\n",
       "Date-Time                                                               \n",
       "2019-08-08 11:18:00+05:30   0.019503   0.019489   0.019474  -0.117906   \n",
       "2019-08-08 11:19:00+05:30   0.019503   0.019489   0.019474  -0.117906   \n",
       "2019-08-08 11:20:00+05:30   0.019503   0.019489   0.019474  -0.117906   \n",
       "2019-08-08 11:21:00+05:30   0.019503   0.019489   0.019474  -0.117906   \n",
       "2019-08-08 11:22:00+05:30   0.019503   0.019489   0.019474  -0.117906   \n",
       "\n",
       "                           MAX_lag_2  MAX_lag_3  \n",
       "Date-Time                                        \n",
       "2019-08-08 11:18:00+05:30   -0.11792  -0.117935  \n",
       "2019-08-08 11:19:00+05:30   -0.11792  -0.117935  \n",
       "2019-08-08 11:20:00+05:30   -0.11792  -0.117935  \n",
       "2019-08-08 11:21:00+05:30   -0.11792  -0.117935  \n",
       "2019-08-08 11:22:00+05:30   -0.11792  -0.117935  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6009db40",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64e5b33",
   "metadata": {},
   "source": [
    "- SelectKBest : [link](https://www.jianshu.com/p/586ba8c96a3d)\n",
    "    - f_classif 利用ANOVA方法来给特征打分，方差分析法\n",
    "    - get_support():  获取所选特征的掩码或整数索引 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ebadd653",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest, f_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c74c218a",
   "metadata": {},
   "outputs": [],
   "source": [
    "selector = SelectKBest(f_classif, k=1).fit(train_[cols], train['D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "959b95c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False,  True, False, False, False, False, False])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = selector.get_support()\n",
    "indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bdafa6c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MIN_lag_1'], dtype='<U12')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_best = np.array(cols)[indices]\n",
    "k_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "56eabf22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Price_lag_1',\n",
       " 'Price_lag_2',\n",
       " 'Price_lag_3',\n",
       " 'Volume_lag_1',\n",
       " 'Volume_lag_2',\n",
       " 'Volume_lag_3',\n",
       " 'RET_lag_1',\n",
       " 'RET_lag_2',\n",
       " 'RET_lag_3',\n",
       " 'SMA1_lag_1',\n",
       " 'SMA1_lag_2',\n",
       " 'SMA1_lag_3',\n",
       " 'SMA2_lag_1',\n",
       " 'SMA2_lag_2',\n",
       " 'SMA2_lag_3',\n",
       " 'MOM_lag_1',\n",
       " 'MOM_lag_2',\n",
       " 'MOM_lag_3',\n",
       " 'VOL_lag_1',\n",
       " 'VOL_lag_2',\n",
       " 'VOL_lag_3',\n",
       " 'MIN_lag_1',\n",
       " 'MIN_lag_2',\n",
       " 'MIN_lag_3',\n",
       " 'MAX_lag_1',\n",
       " 'MAX_lag_2',\n",
       " 'MAX_lag_3']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_ = cols\n",
    "cols_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e92034bb",
   "metadata": {},
   "source": [
    "### Training a DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "90c3cf6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bf4421b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc = DecisionTreeClassifier(max_depth=3, min_samples_split=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2b0f6256",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=15, max_features=0.75,\n",
    "                               max_samples=0.75,\n",
    "                               max_depth=3, min_samples_split=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6ff62f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bc = BaggingClassifier(base_estimator=dtc, n_estimators=100,\n",
    "                           max_samples=0.75,\n",
    "                           max_features=0.75,\n",
    "                           bootstrap=True,\n",
    "                           bootstrap_features=True,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "93f6b821",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = dtc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c94bc2bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 549 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, min_samples_split=15)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time model.fit(train_[cols_], train['D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4f93eaa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.519181550860379"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(train_[cols_], train['D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3327ba44",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['PRED'] = model.predict(train_[cols_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "127032f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1    43578\n",
       " 1    29878\n",
       "Name: PRED, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['PRED'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d99cb11b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['STRAT'] = train['PRED'] * train['RET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ac35623e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RET      0.899973\n",
       "STRAT    2.807396\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['RET', 'STRAT']].sum().apply(np.exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "998f093b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date-Time'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[['RET', 'STRAT']].cumsum().apply(np.exp).plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baacd3a2",
   "metadata": {},
   "source": [
    "### Testing the DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a510db10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.517928612268235"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(test_[cols_], test['D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8d0d138d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['PRED'] = model.predict(test_[cols_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ccc482b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1    14698\n",
       " 1     9788\n",
       "Name: PRED, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['PRED'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5faa0b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['STRAT'] = test['PRED'] * test['RET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7dde59a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RET      1.157427\n",
       "STRAT    1.265797\n",
       "dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[['RET', 'STRAT']].sum().apply(np.exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b53b394d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date-Time'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test[['RET', 'STRAT']].cumsum().apply(np.exp).plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca5c662",
   "metadata": {},
   "source": [
    "### Training a MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "84cb64ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "26b3f14b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MLPClassifier(\n",
    "    hidden_layer_sizes=[16],\n",
    "    random_state=100,\n",
    "    validation_fraction=0.1,\n",
    "    shuffle=False,\n",
    "    max_iter=1000\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b383785b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.91 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=[16], max_iter=1000, random_state=100,\n",
       "              shuffle=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time model.fit(train_[cols_], train['D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "eaee7a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5140764539316053"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(train_[cols_], train['D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "16e9c4e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['PRED'] = model.predict(train_[cols_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "91a86ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1    55269\n",
       " 1    18187\n",
       "Name: PRED, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['PRED'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e27846b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['STRAT'] = train['PRED'] * train['RET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "86fbfac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RET      0.899973\n",
       "STRAT    2.748636\n",
       "dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['RET', 'STRAT']].sum().apply(np.exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a9e3c721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date-Time'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[['RET', 'STRAT']].cumsum().apply(np.exp).plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512180b2",
   "metadata": {},
   "source": [
    "### Testing the MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ffced667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.522666013232051"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(test_[cols_], test['D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "beb1d2e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['PRED'] = model.predict(test_[cols_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "281942ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    13917\n",
       "-1    10569\n",
       "Name: PRED, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['PRED'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "59b70c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['STRAT'] = test['PRED'] * test['RET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "20130c09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RET      1.157427\n",
       "STRAT    1.432471\n",
       "dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[['RET', 'STRAT']].sum().apply(np.exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "374390b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date-Time'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test[['RET', 'STRAT']].cumsum().apply(np.exp).plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a27409a",
   "metadata": {},
   "source": [
    "# 三、Rolling Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fb737509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date-Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:16:00</th>\n",
       "      <td>11078.30</td>\n",
       "      <td>216150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:17:00</th>\n",
       "      <td>11064.55</td>\n",
       "      <td>139350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:18:00</th>\n",
       "      <td>11070.40</td>\n",
       "      <td>127950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:19:00</th>\n",
       "      <td>11078.05</td>\n",
       "      <td>84000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 09:20:00</th>\n",
       "      <td>11082.50</td>\n",
       "      <td>104700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Price    Volume\n",
       "Date-Time                              \n",
       "2019-08-01 09:16:00  11078.30  216150.0\n",
       "2019-08-01 09:17:00  11064.55  139350.0\n",
       "2019-08-01 09:18:00  11070.40  127950.0\n",
       "2019-08-01 09:19:00  11078.05   84000.0\n",
       "2019-08-01 09:20:00  11082.50  104700.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_data = pd.read_csv('nif_refinitiv_resampled.csv', index_col=0, parse_dates=True).iloc[:]\n",
    "\n",
    "resampled_data.index = resampled_data.index.tz_localize(None)\n",
    "resampled_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d1c247",
   "metadata": {},
   "source": [
    "Preparing Features & Labels Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ba90d1d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "resampled_data['RET'] = np.log(resampled_data['Price']/resampled_data['Price'].shift(1))\n",
    "\n",
    "window = 1000\n",
    "resampled_data['SMA1'] = resampled_data['Price'].rolling(window).mean()\n",
    "resampled_data['SMA2'] = resampled_data['Price'].rolling(2 * window).mean()\n",
    "resampled_data['MOM'] = resampled_data['RET'].rolling(window).mean()\n",
    "resampled_data['VOL'] = resampled_data['RET'].rolling(window).std()\n",
    "resampled_data['MIN'] = resampled_data['Price'].rolling(window).min()\n",
    "resampled_data['MAX'] = resampled_data['Price'].rolling(window).max()\n",
    "resampled_data.dropna(inplace=True)\n",
    "\n",
    "resampled_data['D'] = np.sign(resampled_data['RET'])  # labels\n",
    "resampled_data['D'] = resampled_data['D'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7a5ee7f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Price', 'Volume', 'RET', 'SMA1', 'SMA2', 'MOM', 'VOL', 'MIN', 'MAX']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['Price', 'Volume', 'RET', 'SMA1', 'SMA2', 'MOM', 'VOL', 'MIN', 'MAX'][:]\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6c04ab",
   "metadata": {},
   "source": [
    "Lagging the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f80311ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lags = 3\n",
    "\n",
    "cols = []\n",
    "for f in features:\n",
    "    for lag in range(1, lags + 1):\n",
    "        col = f'{f}_lag_{lag}'\n",
    "        resampled_data[col] = resampled_data[f].shift(lag)\n",
    "        cols.append(col)\n",
    "resampled_data.dropna(inplace=True)\n",
    "\n",
    "len(cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df884e7f",
   "metadata": {},
   "source": [
    "### Rolling Windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2fbf8403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-08-11 11:18:00+05:30', '2019-08-18 11:18:00+05:30',\n",
       "               '2019-08-25 11:18:00+05:30', '2019-09-01 11:18:00+05:30',\n",
       "               '2019-09-08 11:18:00+05:30', '2019-09-15 11:18:00+05:30',\n",
       "               '2019-09-22 11:18:00+05:30', '2019-09-29 11:18:00+05:30',\n",
       "               '2019-10-06 11:18:00+05:30', '2019-10-13 11:18:00+05:30',\n",
       "               '2019-10-20 11:18:00+05:30', '2019-10-27 11:18:00+05:30',\n",
       "               '2019-11-03 11:18:00+05:30', '2019-11-10 11:18:00+05:30',\n",
       "               '2019-11-17 11:18:00+05:30', '2019-11-24 11:18:00+05:30',\n",
       "               '2019-12-01 11:18:00+05:30', '2019-12-08 11:18:00+05:30',\n",
       "               '2019-12-15 11:18:00+05:30', '2019-12-22 11:18:00+05:30',\n",
       "               '2019-12-29 11:18:00+05:30', '2020-01-05 11:18:00+05:30',\n",
       "               '2020-01-12 11:18:00+05:30', '2020-01-19 11:18:00+05:30',\n",
       "               '2020-01-26 11:18:00+05:30', '2020-02-02 11:18:00+05:30',\n",
       "               '2020-02-09 11:18:00+05:30', '2020-02-16 11:18:00+05:30',\n",
       "               '2020-02-23 11:18:00+05:30', '2020-03-01 11:18:00+05:30',\n",
       "               '2020-03-08 11:18:00+05:30', '2020-03-15 11:18:00+05:30',\n",
       "               '2020-03-22 11:18:00+05:30', '2020-03-29 11:18:00+05:30',\n",
       "               '2020-04-05 11:18:00+05:30', '2020-04-12 11:18:00+05:30',\n",
       "               '2020-04-19 11:18:00+05:30', '2020-04-26 11:18:00+05:30',\n",
       "               '2020-05-03 11:18:00+05:30', '2020-05-10 11:18:00+05:30',\n",
       "               '2020-05-17 11:18:00+05:30', '2020-05-24 11:18:00+05:30',\n",
       "               '2020-05-31 11:18:00+05:30', '2020-06-07 11:18:00+05:30',\n",
       "               '2020-06-14 11:18:00+05:30', '2020-06-21 11:18:00+05:30',\n",
       "               '2020-06-28 11:18:00+05:30', '2020-07-05 11:18:00+05:30',\n",
       "               '2020-07-12 11:18:00+05:30', '2020-07-19 11:18:00+05:30',\n",
       "               '2020-07-26 11:18:00+05:30', '2020-08-02 11:18:00+05:30',\n",
       "               '2020-08-09 11:18:00+05:30', '2020-08-16 11:18:00+05:30',\n",
       "               '2020-08-23 11:18:00+05:30', '2020-08-30 11:18:00+05:30'],\n",
       "              dtype='datetime64[ns, pytz.FixedOffset(330)]', freq='W-SUN')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeks = pd.date_range(start=resam_data.index.min(), end=resam_data.index.max(), freq='W')\n",
    "weeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cc75a6c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-08-11 11:18:00+05:30', '2019-08-18 11:18:00+05:30',\n",
       "               '2019-08-25 11:18:00+05:30', '2019-09-01 11:18:00+05:30',\n",
       "               '2019-09-08 11:18:00+05:30', '2019-09-15 11:18:00+05:30',\n",
       "               '2019-09-22 11:18:00+05:30', '2019-09-29 11:18:00+05:30',\n",
       "               '2019-10-06 11:18:00+05:30', '2019-10-13 11:18:00+05:30'],\n",
       "              dtype='datetime64[ns, pytz.FixedOffset(330)]', freq=None)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeks.freq=None\n",
    "weeks[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1542c1d",
   "metadata": {},
   "source": [
    "### Rolling Training & Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4394e405",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "47c03a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc = DecisionTreeClassifier(max_depth=3, min_samples_split=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "adc26e60",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=15, max_features=0.75,\n",
    "                                max_samples=0.75,\n",
    "                                max_depth=3, min_samples_split=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ef5fbe9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bc = BaggingClassifier(base_estimator=dtc, n_estimators=15,\n",
    "                            max_samples=0.75,\n",
    "                            max_features=0.75,\n",
    "                            bootstrap=True,\n",
    "                            bootstrap_features=True,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "86d3b3b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(\n",
    "    hidden_layer_sizes=[12],\n",
    "    random_state=100,\n",
    "    validation_fraction=0.1,\n",
    "    shuffle=False,\n",
    "    max_iter=1000 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e0d30560",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = dtc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "94f21d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "resampled_data['PRED'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9690d2d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 24.5 s3  | test score=0.543  | len train=95056\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "for batch in range(12, len(weeks) - 1):\n",
    "    train = resampled_data.loc[weeks[0]:weeks[batch]]\n",
    "    mu, std = train.mean(), train.std()\n",
    "    train_ = (train - mu) / std\n",
    "    model.fit(train_[cols], train['D'])\n",
    "    \n",
    "    test = resampled_data.loc[weeks[batch]: weeks[batch + 1]]\n",
    "    test_ = (test - mu) / std\n",
    "    resampled_data['PRED'].loc[weeks[batch]: weeks[batch + 1]] = model.predict(test_[cols])\n",
    "    print(f'{batch}/{len(weeks)} | ' + str(weeks[batch])[:10],\n",
    "          ' | test score=%.3f' % model.score(test_[cols], test['D']),\n",
    "          ' | len train=%d' % len(train), end='\\r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "639b1b26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1    45192\n",
       " 1    32142\n",
       "Name: PRED, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_data['PRED'][resampled_data['PRED'] != 0].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9b3b30e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27445"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(resampled_data['PRED'][resampled_data['PRED'] != 0].diff() != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d6188232",
   "metadata": {},
   "outputs": [],
   "source": [
    "resampled_data['STRAT'] = resampled_data['PRED'] * resampled_data['RET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "38f253fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RET      0.978716\n",
       "STRAT    2.304887\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_data[['RET', 'STRAT']][resampled_data['PRED'] != 0].sum().apply(np.exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5fa5abf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resampled_data[['RET', 'STRAT']][resampled_data['PRED'] != 0].cumsum().apply(np.exp).plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e942b1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py4fi",
   "language": "python",
   "name": "py4fi"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
